```，归一化时用到的最大值并不是用全局最大值，而是取排序后95%位置处的特征值作为最大值，同时保留极值。
+
+### CTR网络输入的定义
+
+正如前所述，Criteo数据集中，分为连续数据与离散（稀疏）数据，所以整体而言，CTR-DNN模型的数据输入层包括三个，分别是：`dense_input`用于输入连续数据，维度由超参数`dense_feature_dim`指定，数据类型是归一化后的浮点型数据。`sparse_input_ids`用于记录离散数据，在Criteo数据集中，共有26个slot，所以我们创建了名为`C1~C26`的26个稀疏参数输入，并设置`lod_level=1`，代表其为变长数据，数据类型为整数；最后是每条样本的`label`，代表了是否被点击，数据类型是整数，0代表负样例，1代表正样例。
+
+在Paddle中数据输入的声明使用`paddle.fluid.layers.data()`，会创建指定类型的占位符，数据IO会依据此定义进行数据的输入。
+
+稀疏参数输入的定义:
+```python
+def sparse_inputs():
+ ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None, self._namespace)
+
+ sparse_input_ids = [
+ fluid.layers.data(name="S" + str(i),
+ shape=[1],
+ lod_level=1,
+ dtype="int64") for i in range(1, ids)
+ ]
+ return sparse_input_ids
+```
+
+稠密参数输入的定义：
+```python
+def dense_input():
+ dim = envs.get_global_env("hyper_parameters.dense_input_dim", None, self._namespace)
+
+ dense_input_var = fluid.layers.data(name="D",
+ shape=[dim],
+ dtype="float32")
+ return dense_input_var
+```
+
+标签的定义：
+```python
+def label_input():
+ label = fluid.layers.data(name="click", shape=[1], dtype="int64")
+ return label
+```
+
+组合起来，正确的声明他们：
+```python
+self.sparse_inputs = sparse_inputs()
+self.dense_input = dense_input()
+self.label_input = label_input()
+
+self._data_var.append(self.dense_input)
+
+for input in self.sparse_inputs:
+ self._data_var.append(input)
+
+self._data_var.append(self.label_input)
+
+```
+
+
+### Criteo Reader写法
+
+```python
+# 引入PaddleRec的Reader基类
+from paddlerec.core.reader import Reader
+# 引入PaddleRec的读取yaml配置文件的方法
+from paddlerec.core.utils import envs
+
+# 定义TrainReader，需要继承 paddlerec.core.reader.Reader
+class TrainReader(Reader):
+
+ # 数据预处理逻辑，继承自基类
+ # 如果无需处理， 使用pass跳过该函数的执行
+ def init(self):
+ self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+ self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+ self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+ self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
+ self.continuous_range_ = range(1, 14)
+ self.categorical_range_ = range(14, 40)
+
+ # 读取数据方法，继承自基类
+ # 实现可以迭代的reader函数，逐行处理数据
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.rstrip('\n').split('\t')
+
+ dense_feature = []
+ sparse_feature = []
+ for idx in self.continuous_range_:
+ if features[idx] == "":
+ dense_feature.append(0.0)
+ else:
+ dense_feature.append(
+ (float(features[idx]) - self.cont_min_[idx - 1]) /
+ self.cont_diff_[idx - 1])
+
+ for idx in self.categorical_range_:
+ sparse_feature.append(
+ [hash(str(idx) + features[idx]) % self.hash_dim_])
+ label = [int(features[0])]
+ feature_name = ["D"]
+ for idx in self.categorical_range_:
+ feature_name.append("S" + str(idx - 13))
+ feature_name.append("label")
+ yield zip(feature_name, [dense_feature] + sparse_feature + [label])
+
+ return reader
+```
+
+
+### 调试Reader
+
+在Linux下运行时，默认启动`Dataset`模式，在Win/Mac下运行时，默认启动`Dataloader`模式。
+
+通过在`config.yaml`中添加或修改`reader_debug_mode=True`打开debug模式，只会结合组网运行reader的部分，读取10条样本，并print，方便您观察格式是否符合预期或隐藏bug。
+```yaml
+reader:
+ batch_size: 2
+ class: "{workspace}/../criteo_reader.py"
+ train_data_path: "{workspace}/data/train"
+ reader_debug_mode: True
+```
+
+修改后，使用paddlerec.run执行该修改后的yaml文件，可以观察输出。
+```bash
+python -m paddlerec.run -m ./models/rank/dnn/config.yaml -e single
+```
+
+### Dataset调试
+
+dataset输出的数据格式如下：
+` dense_input:size ; dense_input:value ; sparse_input:size ; sparse_input:value ; ... ; sparse_input:size ; sparse_input:value ; label:size ; label:value `
+
+基本规律是对于每个变量，会先输出其维度大小，再输出其具体值。
+
+直接debug `criteo_reader`理想的输出为(截取了一个片段)：
+```bash
+...
+13 0.0 0.00497512437811 0.05 0.08 0.207421875 0.028 0.35 0.08 0.082 0.0 0.4 0.0 0.08 1 737395 1 210498 1 903564 1 286224 1 286835 1 906818 1 90
+6116 1 67180 1 27346 1 51086 1 142177 1 95024 1 157883 1 873363 1 600281 1 812592 1 228085 1 35900 1 880474 1 984402 1 100885 1 26235 1 410878 1 798162 1 499868 1 306163 1 0
+...
+```
+可以看到首先输出的是13维的dense参数，随后是分立的sparse参数，最后一个是1维的label，数值为0，输出符合预期。
+
+>使用Dataset的一些注意事项
+> - Dataset的基本原理：将数据print到缓存，再由C++端的代码实现读取，因此，我们不能在dataset的读取代码中，加入与数据读取无关的print信息，会导致C++端拿到错误的数据信息。
+> - dataset目前只支持在`unbuntu`及`CentOS`等标准Linux环境下使用，在`Windows`及`Mac`下使用时，会产生预料之外的错误，请知悉。
+
+### DataLoader调试
+
+dataloader的输出格式为`list: [ list[var_1], list[var_2], ... , list[var_3]]`，每条样本的数据会被放在一个 **list[list]** 中，list[0]为第一个variable。
+
+直接debug `criteo_reader`理想的输出为(截取了一个片段)：
+```bash
+...
+[[0.0, 0.004975124378109453, 0.05, 0.08, 0.207421875, 0.028, 0.35, 0.08, 0.082, 0.0, 0.4, 0.0, 0.08], [560746], [902436], [262029], [182633], [368411], [735166], [321120], [39572], [185732], [140298], [926671], [81559], [461249], [728372], [915018], [907965], [818961], [850958], [311492], [980340], [254960], [175041], [524857], [764893], [526288], [220126], [0]]
+...
+```
+可以看到首先输出的是13维的dense参数的list，随后是分立的sparse参数，各自在一个list中，最后一个是1维的label的list，数值为0，输出符合预期。
diff --git a/doc/design.md b/doc/design.md
new file mode 100644
index 000000000..2975d77f1
--- /dev/null
+++ b/doc/design.md
@@ -0,0 +1,282 @@
+# PaddleRec 设计
+
+
+## PaddleRec 整体设计概览
+PaddleRec将推荐模型的训练与预测流程，整体抽象为了五个大模块：
+
+* [Engine 流程执行引擎](#engine)
+* [Trainer 流程具体定义](#trainer)
+* [Model 模型组网定义](#model)
+* [Reader 数据读取定义](#reader)
+* [Metric 精度指标打印](#metric)
+
+层级结构，以及一键启动训练时的调用关系如下图所示：
+
+
+
+
+
+core的文件结构如下，后续分别对各个模块进行介绍。
+```
+.core
+├── engine/ 运行引擎实现
+├── metrics/ 全局指标实现
+├── modules/ 自定义op实现
+├── trainers/ 运行流程实现
+├── utils/ 辅助工具
+├── factory.py 运行流程的注册
+├── layer.py 自定义op基类定义
+├── metric.py Metric基类定义
+├── model.py Model基类定义
+├── reader.py Reader基类定义
+└── trainer.py Trainer基类定义
+```
+
+
+## Engine
+
+Engine是整体训练的执行引擎，与组网逻辑及数据无关，只与当前运行模式、运行环境及运行设备有关。
+
+运行模式具体是指：
+- 单机运行
+- 分布式运行
+- 本地模拟分布式
+
+运行环境是指：
+- Linux
+- Windows
+- Mac
+
+运行设备是指：
+- CPU
+- GPU
+- AI芯片
+
+在用户调用`python -m paddlerec.run`时，首先会根据`yaml`文件中的配置信息选择合适的执行引擎， 以下代码位于[run.py](../run.py)：
+```python
+engine_registry()
+which_engine = get_engine(args)
+engine = which_engine(args)
+engine.run()
+```
+
+我们以`single engine`为例，概览engine的行为：
+```python
+def single_engine(args):
+ trainer = get_trainer_prefix(args) + "SingleTrainer"
+ single_envs = {}
+ single_envs["train.trainer.trainer"] = trainer
+ single_envs["train.trainer.threads"] = "2"
+ single_envs["train.trainer.engine"] = "single"
+ single_envs["train.trainer.device"] = args.device
+ single_envs["train.trainer.platform"] = envs.get_platform()
+ print("use {} engine to run model: {}".format(trainer, args.model))
+
+ set_runtime_envs(single_envs, args.model)
+ trainer = TrainerFactory.create(args.model)
+ return trainer
+```
+single_engine被调用后，主要进行了以下两个工作：
+
+1. 根据`yaml`配置文件，设置了**当前进程的环境变量**，后续的所有流程都依赖于环境变量。
+2. 根据模型及环境，指定并初始化了运行流程所用的`Trainer`
+
+进一步细化第一步工作
+- 本地模拟分布式引擎会在单机环境变量的基础上，额外设置本地模拟分布式的环境变量，比如：为各个进程设置不同通信端口，分配ID。最后会启动多个`Trainer`完成本地模拟分布式的工作。
+- 分布式引擎会在单机环境变量的基础上，基于运行参数`-b --backend`所指定的脚本或配置文件，完成分布式任务的文件打包，上传，提交等操作。该脚本格式与分布式任务运行的集群有关，如MPI/K8S/PaddleCloud等，用户可以自定义分布式运行逻辑。
+
+Engine的自定义实现，可以参考[local_cluster.py](../core/engine/local_cluster.py)
+
+## Trainer
+
+`Trainer`是训练与预测流程的具体实现，会run模型中定义的各个流程，与model、reader、metric紧密相关。PaddleRec以有限状态机的逻辑定义了训练中的各个阶段，不同的Trainer子类会分别实现阶段中的特殊需求。有限状态机的流程在`def processor_register()`中注册。
+
+我们以SingleTrainer为例，概览Trainer行为：
+
+```python
+class SingleTrainer(TranspileTrainer):
+ def processor_register(self):
+ self.regist_context_processor('uninit', self.instance)
+ self.regist_context_processor('init_pass', self.init)
+ self.regist_context_processor('startup_pass', self.startup)
+ if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
+ self.regist_context_processor('train_pass', self.dataset_train)
+ else:
+ self.regist_context_processor('train_pass', self.dataloader_train)
+
+ self.regist_context_processor('infer_pass', self.infer)
+ self.regist_context_processor('terminal_pass', self.terminal)
+```
+
+SingleTrainer首先注册了完成任务所需的步骤，各步骤首先按照注册顺序加入`Trainer`基类中名为`status_processor`的字典，运行的先后顺序，可以在每个执行步骤中改变`context['status']`的值，指定下一步运行哪个步骤。
+
+SingleTrainer指定了以下6个步骤：
+1. uninit：默认排在首位，通过环境变量决定model的对象
+1. init_pass：调用model_的接口，生成模型的组网，初始化fetch及metric的变量
+2. startup_pass：初始化模型组网中的各个参数，run(fluid.default_startup_program)
+3. train_pass：会根据环境分别调用`dataset`与`dataloader`进行训练的流程。
+4. infer_pass：在训练结束后，会对训练保存的模型在测试集上验证效果
+5. terminal_pass：打印全局变量及预测结果等自定义的信息。
+
+Trainer的自定义实现，可以参照[single_trainer.py](../core/trainers/single_trainer.py)
+
+## Model
+
+Model定义了各个模型实现的范式，模型只要继承并实现基类中的函数，并给一些成员赋值，就可以保证模型被Trainer正确调用。
+
+我们首先看一下Model基类中的部分重要定义，对模型的实现流程有初步概念。
+
+```python
+class Model(object):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ self._cost = None
+ self._metrics = {}
+ self._data_var = []
+ self._infer_data_var = []
+ self._infer_results = {}
+ self._data_loader = None
+ self._infer_data_loader = None
+ self._fetch_interval = 20
+ self._namespace = "train.model"
+ self._platform = envs.get_platform()
+
+ def get_inputs(self):
+ return self._data_var
+
+ @abc.abstractmethod
+ def train_net(self):
+ pass
+
+ @abc.abstractmethod
+ def infer_net(self):
+ pass
+
+ def get_avg_cost(self):
+ return self._cost
+
+```
+
+每个模型都一定需要继承`def train_net`与`def infer_net`，并且给`self._data_var`与`self._cost`成员赋值，指定模型入口，实现组网的整体逻辑。若有更多或更复杂的需求，可以参照下面的接口，分别继承各个函数，并实现需要的功能：
+
+```python
+def get_infer_inputs(self):
+ return self._infer_data_var
+
+def get_infer_results(self):
+ return self._infer_results
+
+def get_metrics(self):
+ return self._metrics
+
+def get_fetch_period(self):
+ return self._fetch_interval
+```
+
+model的具体实现，可以参考dnn的示例[model.py](../../models/rank/dnn/../../../paddlerec/core/model.py)
+
+
+## Reader
+
+PaddleRec会根据运行环境，分别指定不同的数据IO方式。在Linux下，优先使用`Dataset`，Win及Mac优先使用`Dataloader`。
+
+
+Dataset的使用介绍可以参考[DatasetFactory](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/dataset_cn/DatasetFactory_cn.html)
+
+Dataloader的使用介绍可以参考[异步数据读取](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/data_preparing/use_py_reader.html)
+
+
+考虑到以上两种高效的数据IO方式仍然有很高的学习门槛，PaddleRec将两种数据读取方式进行了更高层次的封装，用户需要实现的仅是每行数据的处理逻辑，剩下的工作交给PaddleRec的Reader基类完成。
+
+首先浏览以下Reader基类的定义，有一个初步的印象：
+
+```python
+class Reader(dg.MultiSlotDataGenerator):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
+ envs.set_global_envs(_config)
+ envs.update_workspace()
+
+ @abc.abstractmethod
+ def init(self):
+ pass
+
+ @abc.abstractmethod
+ def generate_sample(self, line):
+ pass
+
+```
+
+用户需要关注并实现的是`def init(self)`与`def generate_sample(self,line)`函数，分别执行数据读取中预处理所需变量的初始化，以及每一行string的切分及处理逻辑。
+
+当用户定义好以上两个函数，完成自己的Reader后，PaddleRec分别使用
+- [dataset_instance.py](../core/utils/dataset_instance.py)
+- [dataloader_instance.py](../core/utils/dataloader_instance.py)
+
+完成reader的构建工作。
+
+Reader数据处理的逻辑，可以参考[criteo_reader.py](../../models/rank/../../paddlerec/models/rank/criteo_reader.py)
+
+
+
+## Metric
+
+训练必然伴随着训练指标的打印，当单机运行时，打印相关信息比较简单。但分布式训练时，单机指标与全局指标往往有很大diff，比如`auc`以及正逆序`pn`。PaddleRec面向大规模分布式训练，将指标打印的逻辑抽象出来单独实现，以解决分布式训练时全局指标打印的问题。
+
+Metric基类定义了基本的接口，如下：
+```python
+class Metric(object):
+ __metaclass__ = abc.ABCMeta
+
+ def __init__(self, config):
+ """ init """
+ pass
+
+ @abc.abstractmethod
+ def clear(self, scope, params):
+ """
+ clear current value
+ Args:
+ scope: value container
+ params: extend varilable for clear
+ """
+ pass
+
+ @abc.abstractmethod
+ def calculate(self, scope, params):
+ """
+ calculate result
+ Args:
+ scope: value container
+ params: extend varilable for clear
+ """
+ pass
+
+ @abc.abstractmethod
+ def get_result(self):
+ """
+ Return:
+ result(dict) : calculate result
+ """
+ pass
+
+ @abc.abstractmethod
+ def get_result_to_string(self):
+ """
+ Return:
+ result(string) : calculate result with string format, for output
+ """
+ pass
+```
+
+全局指标的计算及输出，需要分别继承并实现以上四个成员函数。具体实现的例子，可以参考[auc_metric.py](../core/metrics/auc_metrics.py)
\ No newline at end of file
diff --git a/doc/distributed_train.md b/doc/distributed_train.md
new file mode 100644
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--- /dev/null
+++ b/doc/distributed_train.md
@@ -0,0 +1,11 @@
+# PaddleRec 分布式训练
+
+## PaddleRec分布式运行
+> 占位
+### 本地模拟分布式
+> 占位
+
+### K8S集群运行分布式
+> 占位
+
+
diff --git a/doc/faq.md b/doc/faq.md
new file mode 100644
index 000000000..f7ca7cc4a
--- /dev/null
+++ b/doc/faq.md
@@ -0,0 +1,2 @@
+# 常见问题FAQ
+> 占位
\ No newline at end of file
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diff --git a/doc/local_train.md b/doc/local_train.md
new file mode 100644
index 000000000..4a43fa552
--- /dev/null
+++ b/doc/local_train.md
@@ -0,0 +1,2 @@
+# PaddleRec 单机训练
+> 占位
\ No newline at end of file
diff --git a/doc/model_list.md b/doc/model_list.md
new file mode 100644
index 000000000..9e68d9f6d
--- /dev/null
+++ b/doc/model_list.md
@@ -0,0 +1,15 @@
+# 支持模型列表
+| 方向 | 模型 | 单机CPU训练 | 单机GPU训练 | 分布式CPU训练 | 大规模稀疏 | 分布式GPU训练 | 自定义数据集 |
+| :------: | :--------------------: | :---------: | :---------: | :-----------: | :--------: | :-----------: | :----------: |
+| 内容理解 | [Text-Classifcation]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 内容理解 | [TagSpace]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 召回 | [Word2Vec]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 召回 | [TDM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [CTR-Dnn]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [DeepFm]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 排序 | [ListWise]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 多任务 | [MMOE]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 多任务 | [ESMM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 匹配 | [DSSM]() | ✓ | x | ✓ | x | ✓ | ✓ |
+| 匹配 | [Multiview-Simnet]() | ✓ | x | ✓ | x | ✓ | ✓ |
+
diff --git a/doc/optimization_model.md b/doc/optimization_model.md
new file mode 100644
index 000000000..b516f8958
--- /dev/null
+++ b/doc/optimization_model.md
@@ -0,0 +1,2 @@
+# PaddleRec 模型调参
+> 占位
\ No newline at end of file
diff --git a/doc/predict.md b/doc/predict.md
new file mode 100644
index 000000000..a33eda43e
--- /dev/null
+++ b/doc/predict.md
@@ -0,0 +1 @@
+# PaddleRec 离线预测
\ No newline at end of file
diff --git a/doc/ps_background.md b/doc/ps_background.md
new file mode 100644
index 000000000..984e1b00c
--- /dev/null
+++ b/doc/ps_background.md
@@ -0,0 +1,8 @@
+## [分布式训练概述](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459124)
+
+
+## [多机多卡训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459127)
+
+
+## [参数服务器训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/464839)
+
diff --git a/doc/rec_background.md b/doc/rec_background.md
new file mode 100644
index 000000000..b6cf8b1c3
--- /dev/null
+++ b/doc/rec_background.md
@@ -0,0 +1,64 @@
+# 推荐系统背景知识
+本文来源于[个性化推荐](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)，进行了节选。
+
+本文代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system),初次使用请您参考[Book文档使用说明](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)。
+
+更多教程及背景知识可以查阅[深度学习实践应用：个性化推荐](https://www.paddlepaddle.org.cn/tutorials/projectdetail/443958)
+
+## 背景介绍
+
+在网络技术不断发展和电子商务规模不断扩大的背景下，商品数量和种类快速增长，用户需要花费大量时间才能找到自己想买的商品，这就是信息超载问题。为了解决这个难题，个性化推荐系统（Recommender System）应运而生。
+
+个性化推荐系统是信息过滤系统（Information Filtering System）的子集，它可以用在很多领域，如电影、音乐、电商和 Feed 流推荐等。个性化推荐系统通过分析、挖掘用户行为，发现用户的个性化需求与兴趣特点，将用户可能感兴趣的信息或商品推荐给用户。与搜索引擎不同，个性化推荐系统不需要用户准确地描述出自己的需求，而是根据用户的历史行为进行建模，主动提供满足用户兴趣和需求的信息。
+
+1994年明尼苏达大学推出的GroupLens系统[[1](#参考文献)]一般被认为是个性化推荐系统成为一个相对独立的研究方向的标志。该系统首次提出了基于协同过滤来完成推荐任务的思想，此后，基于该模型的协同过滤推荐引领了个性化推荐系统十几年的发展方向。
+
+传统的个性化推荐系统方法主要有：
+
+- 协同过滤推荐（Collaborative Filtering Recommendation）：该方法是应用最广泛的技术之一，需要收集和分析用户的历史行为、活动和偏好。它通常可以分为两个子类：基于用户 （User-Based）的推荐[[1](#参考文献)] 和基于物品（Item-Based）的推荐[[2](#参考文献)]。该方法的一个关键优势是它不依赖于机器去分析物品的内容特征，因此它无需理解物品本身也能够准确地推荐诸如电影之类的复杂物品；缺点是对于没有任何行为的新用户存在冷启动的问题，同时也存在用户与商品之间的交互数据不够多造成的稀疏问题。值得一提的是，社交网络[[3](#参考文献)]或地理位置等上下文信息都可以结合到协同过滤中去。
+- 基于内容过滤推荐[[4](#参考文献)]（Content-based Filtering Recommendation）：该方法利用商品的内容描述，抽象出有意义的特征，通过计算用户的兴趣和商品描述之间的相似度，来给用户做推荐。优点是简单直接，不需要依据其他用户对商品的评价，而是通过商品属性进行商品相似度度量，从而推荐给用户所感兴趣商品的相似商品；缺点是对于没有任何行为的新用户同样存在冷启动的问题。
+- 组合推荐[[5](#参考文献)]（Hybrid Recommendation）：运用不同的输入和技术共同进行推荐，以弥补各自推荐技术的缺点。
+
+近些年来，深度学习在很多领域都取得了巨大的成功。学术界和工业界都在尝试将深度学习应用于个性化推荐系统领域中。深度学习具有优秀的自动提取特征的能力，能够学习多层次的抽象特征表示，并对异质或跨域的内容信息进行学习，可以一定程度上处理个性化推荐系统冷启动问题[[6](#参考文献)]。
+
+### YouTube的深度神经网络个性化推荐系统
+
+YouTube是世界上最大的视频上传、分享和发现网站，YouTube个性化推荐系统为超过10亿用户从不断增长的视频库中推荐个性化的内容。整个系统由两个神经网络组成：候选生成网络和排序网络。候选生成网络从百万量级的视频库中生成上百个候选，排序网络对候选进行打分排序，输出排名最高的数十个结果。系统结构如图1所示：
+
+
+
+图1. YouTube 个性化推荐系统结构
+
+
+#### 候选生成网络（Candidate Generation Network）
+
+候选生成网络将推荐问题建模为一个类别数极大的多类分类问题：对于一个Youtube用户，使用其观看历史（视频ID）、搜索词记录（search tokens）、人口学信息（如地理位置、用户登录设备）、二值特征（如性别，是否登录）和连续特征（如用户年龄）等，对视频库中所有视频进行多分类，得到每一类别的分类结果（即每一个视频的推荐概率），最终输出概率较高的几百个视频。
+
+首先，将观看历史及搜索词记录这类历史信息，映射为向量后取平均值得到定长表示；同时，输入人口学特征以优化新用户的推荐效果，并将二值特征和连续特征归一化处理到[0, 1]范围。接下来，将所有特征表示拼接为一个向量，并输入给非线形多层感知器（MLP，详见[识别数字](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md)教程）处理。最后，训练时将MLP的输出给softmax做分类，预测时计算用户的综合特征（MLP的输出）与所有视频的相似度，取得分最高的K个作为候选生成网络的筛选结果。
+
+#### 排序网络（Ranking Network）
+排序网络的结构类似于候选生成网络，但是它的目标是对候选进行更细致的打分排序。和传统广告排序中的特征抽取方法类似，这里也构造了大量的用于视频排序的相关特征（如视频 ID、上次观看时间等）。这些特征的处理方式和候选生成网络类似，不同之处是排序网络的顶部是一个加权逻辑回归（weighted logistic regression），它对所有候选视频进行打分，从高到底排序后将分数较高的一些视频返回给用户。
+
+### 融合推荐模型
+本节会使用卷积神经网络（Convolutional Neural Networks）来学习电影名称的表示。下面会依次介绍文本卷积神经网络以及融合推荐模型。
+
+#### 文本卷积神经网络（CNN）
+
+卷积神经网络经常用来处理具有类似网格拓扑结构（grid-like topology）的数据。例如，图像可以视为二维网格的像素点，自然语言可以视为一维的词序列。卷积神经网络可以提取多种局部特征，并对其进行组合抽象得到更高级的特征表示。实验表明，卷积神经网络能高效地对图像及文本问题进行建模处理。
+
+卷积神经网络主要由卷积（convolution）和池化（pooling）操作构成，其应用及组合方式灵活多变，种类繁多。
+
+
+
+## 参考文献
+
+1. P. Resnick, N. Iacovou, etc. “[GroupLens: An Open Architecture for Collaborative Filtering of Netnews](http://ccs.mit.edu/papers/CCSWP165.html)”, Proceedings of ACM Conference on Computer Supported Cooperative Work, CSCW 1994. pp.175-186.
+2. Sarwar, Badrul, et al. "[Item-based collaborative filtering recommendation algorithms.](http://files.grouplens.org/papers/www10_sarwar.pdf)" *Proceedings of the 10th international conference on World Wide Web*. ACM, 2001.
+3. Kautz, Henry, Bart Selman, and Mehul Shah. "[Referral Web: combining social networks and collaborative filtering.](http://www.cs.cornell.edu/selman/papers/pdf/97.cacm.refweb.pdf)" Communications of the ACM 40.3 (1997): 63-65. APA
+4. [Peter Brusilovsky](https://en.wikipedia.org/wiki/Peter_Brusilovsky) (2007). *The Adaptive Web*. p. 325.
+5. Robin Burke , [Hybrid Web Recommender Systems](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.435.7538&rep=rep1&type=pdf), pp. 377-408, The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (Ed.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, Vol. 4321, May 2007, 978-3-540-72078-2.
+6. Yuan, Jianbo, et al. ["Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach."](https://arxiv.org/pdf/1611.05480v1.pdf) *arXiv preprint arXiv:1611.05480* (2016).
+
+
+
+
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。
diff --git a/models/__init__.py b/models/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/contentunderstanding/__init__.py b/models/contentunderstanding/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/models/contentunderstanding/classification/config.yaml b/models/contentunderstanding/classification/config.yaml
new file mode 100644
index 000000000..d1748137f
--- /dev/null
+++ b/models/contentunderstanding/classification/config.yaml
@@ -0,0 +1,40 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 10
+ workspace: "paddlerec.models.contentunderstanding.classification"
+
+ reader:
+ batch_size: 5
+ class: "{workspace}/reader.py"
+ train_data_path: "{workspace}/train_data"
+
+ model:
+ models: "{workspace}/model.py"
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 1
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 100
+ save_last: True
+
diff --git a/models/contentunderstanding/classification/model.py b/models/contentunderstanding/classification/model.py
new file mode 100644
index 000000000..9e853aa01
--- /dev/null
+++ b/models/contentunderstanding/classification/model.py
@@ -0,0 +1,74 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ self.dict_dim = 100
+ self.max_len = 10
+ self.cnn_dim = 32
+ self.cnn_filter_size = 128
+ self.emb_dim = 8
+ self.hid_dim = 128
+ self.class_dim = 2
+
+ def train_net(self):
+ """ network definition """
+
+ data = fluid.data(name="input", shape=[None, self.max_len], dtype='int64')
+ label = fluid.data(name="label", shape=[None, 1], dtype='int64')
+ seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
+
+ self._data_var = [data, label, seq_len]
+
+ # embedding layer
+ emb = fluid.embedding(input=data, size=[self.dict_dim, self.emb_dim])
+ emb = fluid.layers.sequence_unpad(emb, length=seq_len)
+ # convolution layer
+ conv = fluid.nets.sequence_conv_pool(
+ input=emb,
+ num_filters=self.cnn_dim,
+ filter_size=self.cnn_filter_size,
+ act="tanh",
+ pool_type="max")
+
+ # full connect layer
+ fc_1 = fluid.layers.fc(input=[conv], size=self.hid_dim)
+ # softmax layer
+ prediction = fluid.layers.fc(input=[fc_1], size=self.class_dim, act="softmax")
+ cost = fluid.layers.cross_entropy(input=prediction, label=label)
+ avg_cost = fluid.layers.mean(x=cost)
+ acc = fluid.layers.accuracy(input=prediction, label=label)
+
+ self.cost = avg_cost
+ self._metrics["acc"] = acc
+
+ def get_avg_cost(self):
+ return self.cost
+
+ def get_metrics(self):
+ return self._metrics
+
+ def optimizer(self):
+ learning_rate = 0.01
+ sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
+ return sgd_optimizer
+
+ def infer_net(self):
+ self.train_net()
diff --git a/models/contentunderstanding/classification/reader.py b/models/contentunderstanding/classification/reader.py
new file mode 100644
index 000000000..136a56688
--- /dev/null
+++ b/models/contentunderstanding/classification/reader.py
@@ -0,0 +1,44 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def _process_line(self, l):
+ l = l.strip().split(" ")
+ data = l[0:10]
+ seq_len = l[10:11]
+ label = l[11:]
+ return data, label, seq_len
+
+ def generate_sample(self, line):
+ def data_iter():
+ data, label, seq_len = self._process_line(line)
+ if data is None:
+ yield None
+ return
+ data = [int(i) for i in data]
+ label = [int(i) for i in label]
+ seq_len = [int(i) for i in seq_len]
+ print >> sys.stderr, str([('data', data), ('label', label), ('seq_len', seq_len)])
+ yield [('data', data), ('label', label), ('seq_len', seq_len)]
+
+ return data_iter
diff --git a/models/contentunderstanding/classification/train_data/part-0 b/models/contentunderstanding/classification/train_data/part-0
new file mode 100644
index 000000000..421b9e90a
--- /dev/null
+++ b/models/contentunderstanding/classification/train_data/part-0
@@ -0,0 +1,320 @@
+12 27 13 0 25 52 89 20 39 4 9 1
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diff --git a/models/contentunderstanding/readme.md b/models/contentunderstanding/readme.md
new file mode 100644
index 000000000..07ec96f24
--- /dev/null
+++ b/models/contentunderstanding/readme.md
@@ -0,0 +1,90 @@
+# 内容理解模型库
+
+## 简介
+我们提供了常见的内容理解任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的内容理解模型包括 [Tagspace](tagspace)、[文本分类](classification)等。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [模型列表](#内容理解模型列表)
+* [使用教程](#使用教程)
+ * [数据处理](#数据处理)
+ * [训练](#训练)
+ * [预测](#预测)
+* [效果对比](#效果对比)
+ * [模型效果列表](#模型效果列表)
+* [分布式](#分布式)
+ * [模型性能列表](#模型性能列表)
+
+## 整体介绍
+### 模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| TagSpace | 标签推荐 | [TagSpace: Semantic Embeddings from Hashtags (2014)](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags/) |
+| Classification | 文本分类 | [Convolutional neural networks for sentence classication (2014)](https://www.aclweb.org/anthology/D14-1181.pdf) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[TagSpace模型](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags)
+
+
+
+
+[文本分类CNN模型](https://www.aclweb.org/anthology/D14-1181.pdf)
+
+
+
+
+## 使用教程
+### 数据处理
+
+**（1）TagSpace**
+
+[数据地址](https://github.com/mhjabreel/CharCNN/tree/master/data/) , [备份数据地址](https://paddle-tagspace.bj.bcebos.com/data.tar)
+
+数据格式如下
+```
+"3","Wall St. Bears Claw Back Into the Black (Reuters)","Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again."
+```
+
+数据解压后，将文本数据转为paddle数据，先将数据放到训练数据目录和测试数据目录
+
+```
+mkdir raw_big_train_data
+mkdir raw_big_test_data
+mv train.csv raw_big_train_data
+mv test.csv raw_big_test_data
+```
+
+运行脚本text2paddle.py 生成paddle输入格式
+
+```
+python text2paddle.py raw_big_train_data/ raw_big_test_data/ train_big_data test_big_data big_vocab_text.txt big_vocab_tag.txt
+```
+
+**（2）Classification**
+
+无
+
+### 训练
+
+```
+python -m paddlerec.run -m paddlerec.models.contentunderstanding.classification
+```
+
+### 预测
+
+```
+python -m paddlerec.run -m paddlerec.models.contentunderstanding.classification
+```
+
+## 效果对比
+### 模型效果 (测试)
+
+| 数据集 | 模型 | loss | auc | acc | mae |
+| :------------------: | :--------------------: | :---------: |:---------: | :---------: |:---------: |
+| ag news dataset | TagSpace | -- | -- | -- | -- |
+| -- | Classification | -- | -- | -- | -- |
+
diff --git a/models/contentunderstanding/tagspace/__init__.py b/models/contentunderstanding/tagspace/__init__.py
new file mode 100644
index 000000000..abf198b97
--- /dev/null
+++ b/models/contentunderstanding/tagspace/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/contentunderstanding/tagspace/config.yaml b/models/contentunderstanding/tagspace/config.yaml
new file mode 100644
index 000000000..70333fcbf
--- /dev/null
+++ b/models/contentunderstanding/tagspace/config.yaml
@@ -0,0 +1,50 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 10
+ workspace: "paddlerec.models.contentunderstanding.tagspace"
+
+ reader:
+ batch_size: 5
+ class: "{workspace}/reader.py"
+ train_data_path: "{workspace}/train_data"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ vocab_text_size: 11447
+ vocab_tag_size: 4
+ emb_dim: 10
+ hid_dim: 1000
+ win_size: 5
+ margin: 0.1
+ neg_size: 3
+ num_devices: 1
+
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 1
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 100
+ save_last: True
+
diff --git a/models/contentunderstanding/tagspace/model.py b/models/contentunderstanding/tagspace/model.py
new file mode 100644
index 000000000..033d51b8f
--- /dev/null
+++ b/models/contentunderstanding/tagspace/model.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+import paddle.fluid.layers.nn as nn
+import paddle.fluid.layers.tensor as tensor
+import paddle.fluid.layers.control_flow as cf
+
+from paddlerec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ self.cost = None
+ self.metrics = {}
+ self.vocab_text_size = envs.get_global_env("vocab_text_size", None, self._namespace)
+ self.vocab_tag_size = envs.get_global_env("vocab_tag_size", None, self._namespace)
+ self.emb_dim = envs.get_global_env("emb_dim", None, self._namespace)
+ self.hid_dim = envs.get_global_env("hid_dim", None, self._namespace)
+ self.win_size = envs.get_global_env("win_size", None, self._namespace)
+ self.margin = envs.get_global_env("margin", None, self._namespace)
+ self.neg_size = envs.get_global_env("neg_size", None, self._namespace)
+
+ def train_net(self):
+ """ network definition """
+ text = fluid.data(name="text", shape=[None, 1], lod_level=1, dtype='int64')
+ pos_tag = fluid.data(
+ name="pos_tag", shape=[None, 1], lod_level=1, dtype='int64')
+ neg_tag = fluid.data(
+ name="neg_tag", shape=[None, 1], lod_level=1, dtype='int64')
+
+ self._data_var = [text, pos_tag, neg_tag]
+
+ text_emb = fluid.embedding(
+ input=text, size=[self.vocab_text_size, self.emb_dim], param_attr="text_emb")
+ text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
+ pos_tag_emb = fluid.embedding(
+ input=pos_tag, size=[self.vocab_tag_size, self.emb_dim], param_attr="tag_emb")
+ pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
+ neg_tag_emb = fluid.embedding(
+ input=neg_tag, size=[self.vocab_tag_size, self.emb_dim], param_attr="tag_emb")
+ neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
+
+ conv_1d = fluid.nets.sequence_conv_pool(
+ input=text_emb,
+ num_filters=self.hid_dim,
+ filter_size=self.win_size,
+ act="tanh",
+ pool_type="max",
+ param_attr="cnn")
+ text_hid = fluid.layers.fc(input=conv_1d,
+ size=self.emb_dim,
+ param_attr="text_hid")
+ cos_pos = nn.cos_sim(pos_tag_emb, text_hid)
+ mul_text_hid = fluid.layers.sequence_expand_as(x=text_hid, y=neg_tag_emb)
+ mul_cos_neg = nn.cos_sim(neg_tag_emb, mul_text_hid)
+ cos_neg_all = fluid.layers.sequence_reshape(
+ input=mul_cos_neg, new_dim=self.neg_size)
+ #choose max negtive cosine
+ cos_neg = nn.reduce_max(cos_neg_all, dim=1, keep_dim=True)
+ #calculate hinge loss
+ loss_part1 = nn.elementwise_sub(
+ tensor.fill_constant_batch_size_like(
+ input=cos_pos, shape=[-1, 1], value=self.margin, dtype='float32'),
+ cos_pos)
+ loss_part2 = nn.elementwise_add(loss_part1, cos_neg)
+ loss_part3 = nn.elementwise_max(
+ tensor.fill_constant_batch_size_like(
+ input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
+ loss_part2)
+ avg_cost = nn.mean(loss_part3)
+ less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
+ correct = nn.reduce_sum(less)
+ self.cost = avg_cost
+
+ self.metrics["correct"] = correct
+ self.metrics["cos_pos"] = cos_pos
+
+ def get_avg_cost(self):
+ return self.cost
+
+ def get_metrics(self):
+ return self.metrics
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
+ sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
+ return sgd_optimizer
+
+ def infer_net(self, parameter_list):
+ self.train_net()
diff --git a/models/contentunderstanding/tagspace/reader.py b/models/contentunderstanding/tagspace/reader.py
new file mode 100644
index 000000000..0f63b85fd
--- /dev/null
+++ b/models/contentunderstanding/tagspace/reader.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def _process_line(self, l):
+ tag_size = 4
+ neg_size = 3
+ l = l.strip().split(",")
+ pos_index = int(l[0])
+ pos_tag = []
+ pos_tag.append(pos_index)
+ text_raw = l[1].split()
+ text = [int(w) for w in text_raw]
+ neg_tag = []
+ max_iter = 100
+ now_iter = 0
+ sum_n = 0
+ while (sum_n < neg_size):
+ now_iter += 1
+ if now_iter > max_iter:
+ print("error : only one class")
+ sys.exit(0)
+ rand_i = np.random.randint(0, tag_size)
+ if rand_i != pos_index:
+ neg_index = rand_i
+ neg_tag.append(neg_index)
+ sum_n += 1
+ return text, pos_tag, neg_tag
+
+ def generate_sample(self, line):
+ def data_iter():
+ text, pos_tag, neg_tag = self._process_line(line)
+ if text is None:
+ yield None
+ return
+ yield [('text', text), ('pos_tag', pos_tag), ('neg_tag', neg_tag)]
+
+ return data_iter
diff --git a/models/contentunderstanding/tagspace/test_data/small_test.csv b/models/contentunderstanding/tagspace/test_data/small_test.csv
new file mode 100644
index 000000000..a88f023c7
--- /dev/null
+++ b/models/contentunderstanding/tagspace/test_data/small_test.csv
@@ -0,0 +1,1000 @@
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diff --git a/models/contentunderstanding/tagspace/train_data/small_train.csv b/models/contentunderstanding/tagspace/train_data/small_train.csv
new file mode 100644
index 000000000..f35586b45
--- /dev/null
+++ b/models/contentunderstanding/tagspace/train_data/small_train.csv
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diff --git a/models/match/__init__.py b/models/match/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/models/match/dssm/__init__.py b/models/match/dssm/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/match/dssm/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/match/dssm/config.yaml b/models/match/dssm/config.yaml
new file mode 100755
index 000000000..22881bdf9
--- /dev/null
+++ b/models/match/dssm/config.yaml
@@ -0,0 +1,54 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+evaluate:
+ reader:
+ batch_size: 1
+ class: "{workspace}/synthetic_evaluate_reader.py"
+ test_data_path: "{workspace}/data/train"
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 4
+ workspace: "paddlerec.models.match.dssm"
+
+ reader:
+ batch_size: 4
+ class: "{workspace}/synthetic_reader.py"
+ train_data_path: "{workspace}/data/train"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ TRIGRAM_D: 1000
+ NEG: 4
+ fc_sizes: [300, 300, 128]
+ fc_acts: ['tanh', 'tanh', 'tanh']
+ learning_rate: 0.01
+ optimizer: sgd
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ feed_varnames: ["query", "doc_pos"]
+ fetch_varnames: ["cos_sim_0.tmp_0"]
+ save_last: True
diff --git a/models/match/dssm/data/train/sample_train.txt b/models/match/dssm/data/train/sample_train.txt
new file mode 100755
index 000000000..11eec4f49
--- /dev/null
+++ b/models/match/dssm/data/train/sample_train.txt
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diff --git a/models/match/dssm/model.py b/models/match/dssm/model.py
new file mode 100755
index 000000000..630fb3eee
--- /dev/null
+++ b/models/match/dssm/model.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def input(self):
+ TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
+ Neg = envs.get_global_env("hyper_parameters.NEG", None, self._namespace)
+
+ self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i
+ in range(Neg)]
+ self._data_var.append(self.query)
+ self._data_var.append(self.doc_pos)
+ for input in self.doc_negs:
+ self._data_var.append(input)
+
+ if self._platform != "LINUX":
+ self._data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+ def net(self, is_infer=False):
+ hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
+ hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None, self._namespace)
+
+ def fc(data, hidden_layers, hidden_acts, names):
+ fc_inputs = [data]
+ for i in range(len(hidden_layers)):
+ xavier = fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
+ out = fluid.layers.fc(input=fc_inputs[-1],
+ size=hidden_layers[i],
+ act=hidden_acts[i],
+ param_attr=xavier,
+ bias_attr=xavier,
+ name=names[i])
+ fc_inputs.append(out)
+ return fc_inputs[-1]
+
+ query_fc = fc(self.query, hidden_layers, hidden_acts, ['query_l1', 'query_l2', 'query_l3'])
+ doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
+ self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
+
+ if is_infer:
+ return
+
+ R_Q_D_ns = []
+ for i, doc_neg in enumerate(self.doc_negs):
+ doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts,
+ ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
+ R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
+ concat_Rs = fluid.layers.concat(input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
+ prob = fluid.layers.softmax(concat_Rs, axis=1)
+
+ hit_prob = fluid.layers.slice(prob, axes=[0, 1], starts=[0, 0], ends=[4, 1])
+ loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
+ self.avg_cost = fluid.layers.mean(x=loss)
+
+ def infer_results(self):
+ self._infer_results['query_doc_sim'] = self.R_Q_D_p
+
+ def avg_loss(self):
+ self._cost = self.avg_cost
+
+ def metrics(self):
+ self._metrics["LOSS"] = self.avg_cost
+
+ def train_net(self):
+ self.input()
+ self.net(is_infer=False)
+ self.avg_loss()
+ self.metrics()
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ optimizer = fluid.optimizer.SGD(learning_rate)
+ return optimizer
+
+ def infer_input(self):
+ TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
+ self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+ self._infer_data_var = [self.query, self.doc_pos]
+
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+ def infer_net(self):
+ self.infer_input()
+ self.net(is_infer=True)
+ self.infer_results()
diff --git a/models/match/dssm/synthetic_evaluate_reader.py b/models/match/dssm/synthetic_evaluate_reader.py
new file mode 100755
index 000000000..97f50abf9
--- /dev/null
+++ b/models/match/dssm/synthetic_evaluate_reader.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.rstrip('\n').split('\t')
+ query = map(float, features[0].split(','))
+ pos_doc = map(float, features[1].split(','))
+ feature_names = ['query', 'doc_pos']
+
+ yield zip(feature_names, [query] + [pos_doc])
+
+ return reader
diff --git a/models/match/dssm/synthetic_reader.py b/models/match/dssm/synthetic_reader.py
new file mode 100755
index 000000000..13f57a666
--- /dev/null
+++ b/models/match/dssm/synthetic_reader.py
@@ -0,0 +1,44 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.rstrip('\n').split('\t')
+ query = map(float, features[0].split(','))
+ pos_doc = map(float, features[1].split(','))
+ feature_names = ['query', 'doc_pos']
+ neg_docs = []
+ for i in range(len(features) - 2):
+ feature_names.append('doc_neg_' + str(i))
+ neg_docs.append(map(float, features[i + 2].split(',')))
+
+ yield zip(feature_names, [query] + [pos_doc] + neg_docs)
+
+ return reader
diff --git a/models/match/multiview-simnet/__init__.py b/models/match/multiview-simnet/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/match/multiview-simnet/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/match/multiview-simnet/config.yaml b/models/match/multiview-simnet/config.yaml
new file mode 100755
index 000000000..53ac4c095
--- /dev/null
+++ b/models/match/multiview-simnet/config.yaml
@@ -0,0 +1,59 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+evaluate:
+ workspace: "paddlerec.models.match.multiview-simnet"
+ reader:
+ batch_size: 2
+ class: "{workspace}/evaluate_reader.py"
+ test_data_path: "{workspace}/data/test"
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 2
+ workspace: "paddlerec.models.match.multiview-simnet"
+
+ reader:
+ batch_size: 2
+ class: "{workspace}/reader.py"
+ train_data_path: "{workspace}/data/train"
+ dataset_class: "DataLoader"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ use_DataLoader: True
+ query_encoder: "bow"
+ title_encoder: "bow"
+ query_encode_dim: 128
+ title_encode_dim: 128
+ query_slots: 1
+ title_slots: 1
+ sparse_feature_dim: 1000001
+ embedding_dim: 128
+ hidden_size: 128
+ learning_rate: 0.0001
+ optimizer: adam
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 1
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 1
+ save_last: True
diff --git a/models/match/multiview-simnet/data/test/test.txt b/models/match/multiview-simnet/data/test/test.txt
new file mode 100755
index 000000000..cfb0675ff
--- /dev/null
+++ b/models/match/multiview-simnet/data/test/test.txt
@@ -0,0 +1,10 @@
+224289:0 126379:0 284519:0 549329:0 750666:0 393772:0 586898:0 736887:0 48785:0 906517:0 229162:1 483485:1 739835:1 29957:1 694497:1 997508:1 556876:1 717791:1 232176:1 430356:1
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+969571:0 405187:0 756217:0 563640:0 572168:0 881952:0 446260:0 692177:0 994140:0 485393:0 509081:1 297377:1 465399:1 934708:1 430949:1 135651:1 484531:1 385306:1 463957:1 996004:1
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+228207:0 785250:0 661149:0 803304:0 478781:0 495202:0 804509:0 273065:0 26123:0 810840:0 801871:1 146772:1 421009:1 752344:1 946358:1 531668:1 5771:1 191294:1 627329:1 434664:1
+984628:0 762075:0 505288:0 48519:0 72492:0 26568:0 684085:0 613095:0 781547:0 895829:0 280541:1 903234:1 708065:1 386658:1 331060:1 3693:1 279760:1 459579:1 423552:1 962594:1
+674172:0 39271:0 646093:0 757969:0 553251:0 734960:0 967186:0 856940:0 617246:0 376452:0 113050:1 472707:1 975057:1 865095:1 155824:1 389921:1 205520:1 513667:1 163588:1 953463:1
diff --git a/models/match/multiview-simnet/data/train/train.txt b/models/match/multiview-simnet/data/train/train.txt
new file mode 100755
index 000000000..4b26c6c6c
--- /dev/null
+++ b/models/match/multiview-simnet/data/train/train.txt
@@ -0,0 +1,10 @@
+7688:0 589671:0 339543:0 681723:0 339204:0 743067:0 897959:0 897541:0 571340:0 858141:0 68161:1 533957:1 288065:1 755516:1 179906:1 324817:1 116293:1 942079:1 455277:1 787142:1 251765:2 846187:2 586960:2 781883:2 430436:2 240100:2 686201:2 632045:2 585097:2 61976:2
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+327559:0 627497:0 876526:0 243959:0 532929:0 639919:0 443220:0 952110:0 844723:0 372053:0 196819:1 326005:1 62242:1 774928:1 382727:1 348680:1 946697:1 625998:1 276517:1 251595:1 342204:2 825871:2 407136:2 724114:2 611341:2 517978:2 248341:2 111254:2 836867:2 677297:2
+72451:0 749548:0 283413:0 419402:0 67446:0 341795:0 918120:0 892028:0 113151:0 832663:0 758121:1 500602:1 734935:1 577972:1 205421:1 726739:1 276563:1 611928:1 185486:1 603502:1 633117:2 929300:2 332435:2 216848:2 412769:2 708304:2 800045:2 315869:2 444476:2 332565:2
+675647:0 212558:0 654982:0 321053:0 111172:0 635432:0 298523:0 612182:0 203835:0 288250:0 990034:1 891786:1 188524:1 480757:1 436783:1 874434:1 530090:1 492441:1 32835:1 886415:1 688876:2 626030:2 612348:2 208265:2 355885:2 603938:2 349931:2 86683:2 361956:2 705130:2
+164500:0 332294:0 373155:0 320413:0 801561:0 152827:0 28282:0 435913:0 376758:0 367848:0 285596:1 282674:1 357323:1 257195:1 948061:1 996976:1 300918:1 734644:1 870559:1 924205:1 45095:2 61352:2 242258:2 153354:2 763576:2 133542:2 431079:2 193327:2 655823:2 770159:2
+821764:0 184731:0 888413:0 793536:0 30049:0 533675:0 791254:0 92255:0 74185:0 557758:0 795898:1 15689:1 983592:1 248891:1 64421:1 387642:1 315522:1 526054:1 404172:1 704838:1 537016:2 383828:2 438418:2 885895:2 894698:2 228867:2 343213:2 411377:2 149957:2 810795:2
diff --git a/models/match/multiview-simnet/data_process.sh b/models/match/multiview-simnet/data_process.sh
new file mode 100755
index 000000000..91548c586
--- /dev/null
+++ b/models/match/multiview-simnet/data_process.sh
@@ -0,0 +1,25 @@
+#! /bin/bash
+
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+set -e
+echo "begin to prepare data"
+
+mkdir -p data/train
+mkdir -p data/test
+
+python generate_synthetic_data.py
+
diff --git a/models/match/multiview-simnet/evaluate_reader.py b/models/match/multiview-simnet/evaluate_reader.py
new file mode 100755
index 000000000..e0f8f9e43
--- /dev/null
+++ b/models/match/multiview-simnet/evaluate_reader.py
@@ -0,0 +1,55 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
+ self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
+
+ self.all_slots = []
+ for i in range(self.query_slots):
+ self.all_slots.append(str(i))
+
+ for i in range(self.title_slots):
+ self.all_slots.append(str(i + self.query_slots))
+
+ self._all_slots_dict = dict()
+ for index, slot in enumerate(self.all_slots):
+ self._all_slots_dict[slot] = [False, index]
+
+ def generate_sample(self, line):
+ def data_iter():
+ elements = line.rstrip().split()
+ padding = 0
+ output = [(slot, []) for slot in self.all_slots]
+ for elem in elements:
+ feasign, slot = elem.split(':')
+ if not self._all_slots_dict.has_key(slot):
+ continue
+ self._all_slots_dict[slot][0] = True
+ index = self._all_slots_dict[slot][1]
+ output[index][1].append(int(feasign))
+ for slot in self._all_slots_dict:
+ visit, index = self._all_slots_dict[slot]
+ if visit:
+ self._all_slots_dict[slot][0] = False
+ else:
+ output[index][1].append(padding)
+ yield output
+
+ return data_iter
diff --git a/models/match/multiview-simnet/generate_synthetic_data.py b/models/match/multiview-simnet/generate_synthetic_data.py
new file mode 100755
index 000000000..d453e031c
--- /dev/null
+++ b/models/match/multiview-simnet/generate_synthetic_data.py
@@ -0,0 +1,91 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+
+
+class Dataset:
+ def __init__(self):
+ pass
+
+
+class SyntheticDataset(Dataset):
+ def __init__(self, sparse_feature_dim, query_slot_num, title_slot_num, dataset_size=10000):
+ # ids are randomly generated
+ self.ids_per_slot = 10
+ self.sparse_feature_dim = sparse_feature_dim
+ self.query_slot_num = query_slot_num
+ self.title_slot_num = title_slot_num
+ self.dataset_size = dataset_size
+
+ def _reader_creator(self, is_train):
+ def generate_ids(num, space):
+ return [random.randint(0, space - 1) for i in range(num)]
+
+ def reader():
+ for i in range(self.dataset_size):
+ query_slots = []
+ pos_title_slots = []
+ neg_title_slots = []
+ for i in range(self.query_slot_num):
+ qslot = generate_ids(self.ids_per_slot,
+ self.sparse_feature_dim)
+ qslot = [str(fea) + ':' + str(i) for fea in qslot]
+ query_slots += qslot
+ for i in range(self.title_slot_num):
+ pt_slot = generate_ids(self.ids_per_slot,
+ self.sparse_feature_dim)
+ pt_slot = [str(fea) + ':' + str(i + self.query_slot_num) for fea in pt_slot]
+ pos_title_slots += pt_slot
+ if is_train:
+ for i in range(self.title_slot_num):
+ nt_slot = generate_ids(self.ids_per_slot,
+ self.sparse_feature_dim)
+ nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in
+ nt_slot]
+ neg_title_slots += nt_slot
+ yield query_slots + pos_title_slots + neg_title_slots
+ else:
+ yield query_slots + pos_title_slots
+
+ return reader
+
+ def train(self):
+ return self._reader_creator(True)
+
+ def valid(self):
+ return self._reader_creator(True)
+
+ def test(self):
+ return self._reader_creator(False)
+
+
+if __name__ == '__main__':
+ sparse_feature_dim = 1000001
+ query_slots = 1
+ title_slots = 1
+ dataset_size = 10
+ dataset = SyntheticDataset(sparse_feature_dim, query_slots, title_slots, dataset_size)
+ train_reader = dataset.train()
+ test_reader = dataset.test()
+
+ with open("data/train/train.txt", 'w') as fout:
+ for data in train_reader():
+ fout.write(' '.join(data))
+ fout.write("\n")
+
+ with open("data/test/test.txt", 'w') as fout:
+ for data in test_reader():
+ fout.write(' '.join(data))
+ fout.write("\n")
diff --git a/models/match/multiview-simnet/model.py b/models/match/multiview-simnet/model.py
new file mode 100755
index 000000000..5ba9fb5d0
--- /dev/null
+++ b/models/match/multiview-simnet/model.py
@@ -0,0 +1,301 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+import paddle.fluid.layers.tensor as tensor
+import paddle.fluid.layers.control_flow as cf
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class BowEncoder(object):
+ """ bow-encoder """
+
+ def __init__(self):
+ self.param_name = ""
+
+ def forward(self, emb):
+ return fluid.layers.sequence_pool(input=emb, pool_type='sum')
+
+
+class CNNEncoder(object):
+ """ cnn-encoder"""
+
+ def __init__(self,
+ param_name="cnn",
+ win_size=3,
+ ksize=128,
+ act='tanh',
+ pool_type='max'):
+ self.param_name = param_name
+ self.win_size = win_size
+ self.ksize = ksize
+ self.act = act
+ self.pool_type = pool_type
+
+ def forward(self, emb):
+ return fluid.nets.sequence_conv_pool(
+ input=emb,
+ num_filters=self.ksize,
+ filter_size=self.win_size,
+ act=self.act,
+ pool_type=self.pool_type,
+ param_attr=self.param_name + ".param",
+ bias_attr=self.param_name + ".bias")
+
+
+class GrnnEncoder(object):
+ """ grnn-encoder """
+
+ def __init__(self, param_name="grnn", hidden_size=128):
+ self.param_name = param_name
+ self.hidden_size = hidden_size
+
+ def forward(self, emb):
+ fc0 = fluid.layers.fc(input=emb,
+ size=self.hidden_size * 3,
+ param_attr=self.param_name + "_fc.w",
+ bias_attr=False)
+
+ gru_h = fluid.layers.dynamic_gru(
+ input=fc0,
+ size=self.hidden_size,
+ is_reverse=False,
+ param_attr=self.param_name + ".param",
+ bias_attr=self.param_name + ".bias")
+ return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+
+
+class SimpleEncoderFactory(object):
+ def __init__(self):
+ pass
+
+ ''' create an encoder through create function '''
+
+ def create(self, enc_type, enc_hid_size):
+ if enc_type == "bow":
+ bow_encode = BowEncoder()
+ return bow_encode
+ elif enc_type == "cnn":
+ cnn_encode = CNNEncoder(ksize=enc_hid_size)
+ return cnn_encode
+ elif enc_type == "gru":
+ rnn_encode = GrnnEncoder(hidden_size=enc_hid_size)
+ return rnn_encode
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+ self.init_config()
+
+ def init_config(self):
+ self._fetch_interval = 1
+ query_encoder = envs.get_global_env("hyper_parameters.query_encoder", None, self._namespace)
+ title_encoder = envs.get_global_env("hyper_parameters.title_encoder", None, self._namespace)
+ query_encode_dim = envs.get_global_env("hyper_parameters.query_encode_dim", None, self._namespace)
+ title_encode_dim = envs.get_global_env("hyper_parameters.title_encode_dim", None, self._namespace)
+ query_slots = envs.get_global_env("hyper_parameters.query_slots", None, self._namespace)
+ title_slots = envs.get_global_env("hyper_parameters.title_slots", None, self._namespace)
+ factory = SimpleEncoderFactory()
+ self.query_encoders = [
+ factory.create(query_encoder, query_encode_dim)
+ for i in range(query_slots)
+ ]
+ self.title_encoders = [
+ factory.create(title_encoder, title_encode_dim)
+ for i in range(title_slots)
+ ]
+
+ self.emb_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
+ self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim", None, self._namespace)
+ self.emb_shape = [self.emb_size, self.emb_dim]
+ self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
+ self.margin = 0.1
+
+ def input(self, is_train=True):
+ self.q_slots = [
+ fluid.data(
+ name="%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
+ for i in range(len(self.query_encoders))
+ ]
+ self.pt_slots = [
+ fluid.data(
+ name="%d" % (i + len(self.query_encoders)), shape=[None, 1], lod_level=1, dtype='int64')
+ for i in range(len(self.title_encoders))
+ ]
+
+ if is_train == False:
+ return self.q_slots + self.pt_slots
+
+ self.nt_slots = [
+ fluid.data(
+ name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1,
+ dtype='int64')
+ for i in range(len(self.title_encoders))
+ ]
+
+ return self.q_slots + self.pt_slots + self.nt_slots
+
+ def train_input(self):
+ res = self.input()
+ self._data_var = res
+
+ use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
+
+ if self._platform != "LINUX" or use_dataloader:
+ self._data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._data_var, capacity=256, use_double_buffer=False, iterable=False)
+
+ def get_acc(self, x, y):
+ less = tensor.cast(cf.less_than(x, y), dtype='float32')
+ label_ones = fluid.layers.fill_constant_batch_size_like(
+ input=x, dtype='float32', shape=[-1, 1], value=1.0)
+ correct = fluid.layers.reduce_sum(less)
+ total = fluid.layers.reduce_sum(label_ones)
+ acc = fluid.layers.elementwise_div(correct, total)
+ return acc
+
+ def net(self):
+ q_embs = [
+ fluid.embedding(
+ input=query, size=self.emb_shape, param_attr="emb")
+ for query in self.q_slots
+ ]
+ pt_embs = [
+ fluid.embedding(
+ input=title, size=self.emb_shape, param_attr="emb")
+ for title in self.pt_slots
+ ]
+ nt_embs = [
+ fluid.embedding(
+ input=title, size=self.emb_shape, param_attr="emb")
+ for title in self.nt_slots
+ ]
+
+ # encode each embedding field with encoder
+ q_encodes = [
+ self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
+ ]
+ pt_encodes = [
+ self.title_encoders[i].forward(emb) for i, emb in enumerate(pt_embs)
+ ]
+ nt_encodes = [
+ self.title_encoders[i].forward(emb) for i, emb in enumerate(nt_embs)
+ ]
+
+ # concat multi view for query, pos_title, neg_title
+ q_concat = fluid.layers.concat(q_encodes)
+ pt_concat = fluid.layers.concat(pt_encodes)
+ nt_concat = fluid.layers.concat(nt_encodes)
+
+ # projection of hidden layer
+ q_hid = fluid.layers.fc(q_concat,
+ size=self.hidden_size,
+ param_attr='q_fc.w',
+ bias_attr='q_fc.b')
+ pt_hid = fluid.layers.fc(pt_concat,
+ size=self.hidden_size,
+ param_attr='t_fc.w',
+ bias_attr='t_fc.b')
+ nt_hid = fluid.layers.fc(nt_concat,
+ size=self.hidden_size,
+ param_attr='t_fc.w',
+ bias_attr='t_fc.b')
+
+ # cosine of hidden layers
+ cos_pos = fluid.layers.cos_sim(q_hid, pt_hid)
+ cos_neg = fluid.layers.cos_sim(q_hid, nt_hid)
+
+ # pairwise hinge_loss
+ loss_part1 = fluid.layers.elementwise_sub(
+ tensor.fill_constant_batch_size_like(
+ input=cos_pos,
+ shape=[-1, 1],
+ value=self.margin,
+ dtype='float32'),
+ cos_pos)
+
+ loss_part2 = fluid.layers.elementwise_add(loss_part1, cos_neg)
+
+ loss_part3 = fluid.layers.elementwise_max(
+ tensor.fill_constant_batch_size_like(
+ input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
+ loss_part2)
+
+ self.avg_cost = fluid.layers.mean(loss_part3)
+ self.acc = self.get_acc(cos_neg, cos_pos)
+
+ def avg_loss(self):
+ self._cost = self.avg_cost
+
+ def metrics(self):
+ self._metrics["loss"] = self.avg_cost
+ self._metrics["acc"] = self.acc
+
+ def train_net(self):
+ self.train_input()
+ self.net()
+ self.avg_loss()
+ self.metrics()
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
+ return optimizer
+
+ def infer_input(self):
+ res = self.input(is_train=False)
+ self._infer_data_var = res
+
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+ def infer_net(self):
+ self.infer_input()
+ # lookup embedding for each slot
+ q_embs = [
+ fluid.embedding(
+ input=query, size=self.emb_shape, param_attr="emb")
+ for query in self.q_slots
+ ]
+ pt_embs = [
+ fluid.embedding(
+ input=title, size=self.emb_shape, param_attr="emb")
+ for title in self.pt_slots
+ ]
+ # encode each embedding field with encoder
+ q_encodes = [
+ self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
+ ]
+ pt_encodes = [
+ self.title_encoders[i].forward(emb) for i, emb in enumerate(pt_embs)
+ ]
+ # concat multi view for query, pos_title, neg_title
+ q_concat = fluid.layers.concat(q_encodes)
+ pt_concat = fluid.layers.concat(pt_encodes)
+ # projection of hidden layer
+ q_hid = fluid.layers.fc(q_concat,
+ size=self.hidden_size,
+ param_attr='q_fc.w',
+ bias_attr='q_fc.b')
+ pt_hid = fluid.layers.fc(pt_concat,
+ size=self.hidden_size,
+ param_attr='t_fc.w',
+ bias_attr='t_fc.b')
+
+ # cosine of hidden layers
+ cos = fluid.layers.cos_sim(q_hid, pt_hid)
+ self._infer_results['query_pt_sim'] = cos
diff --git a/models/match/multiview-simnet/reader.py b/models/match/multiview-simnet/reader.py
new file mode 100755
index 000000000..43cd1a629
--- /dev/null
+++ b/models/match/multiview-simnet/reader.py
@@ -0,0 +1,58 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
+class TrainReader(Reader):
+ def init(self):
+ self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
+ self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
+
+ self.all_slots = []
+ for i in range(self.query_slots):
+ self.all_slots.append(str(i))
+
+ for i in range(self.title_slots):
+ self.all_slots.append(str(i + self.query_slots))
+
+ for i in range(self.title_slots):
+ self.all_slots.append(str(i + self.query_slots + self.title_slots))
+
+ self._all_slots_dict = dict()
+ for index, slot in enumerate(self.all_slots):
+ self._all_slots_dict[slot] = [False, index]
+
+ def generate_sample(self, line):
+ def data_iter():
+ elements = line.rstrip().split()
+ padding = 0
+ output = [(slot, []) for slot in self.all_slots]
+ for elem in elements:
+ feasign, slot = elem.split(':')
+ if not self._all_slots_dict.has_key(slot):
+ continue
+ self._all_slots_dict[slot][0] = True
+ index = self._all_slots_dict[slot][1]
+ output[index][1].append(int(feasign))
+ for slot in self._all_slots_dict:
+ visit, index = self._all_slots_dict[slot]
+ if visit:
+ self._all_slots_dict[slot][0] = False
+ else:
+ output[index][1].append(padding)
+ yield output
+
+ return data_iter
diff --git a/models/match/readme.md b/models/match/readme.md
new file mode 100755
index 000000000..6bccc109f
--- /dev/null
+++ b/models/match/readme.md
@@ -0,0 +1,40 @@
+# 匹配模型库
+
+## 简介
+我们提供了常见的匹配任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的模型包括 [DSSM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/dssm)、[MultiView-Simnet](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/multiview-simnet)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [匹配模型列表](#匹配模型列表)
+* [使用教程](#使用教程)
+ * [训练&预测](#训练&预测)
+
+## 整体介绍
+### 匹配模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| DSSM | Deep Structured Semantic Models | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf)(2013) |
+| MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf)(2015) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+[DSSM](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf):
+
+
+
+
+[MultiView-Simnet](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf):
+
+
+
+
+## 使用教程
+### 训练&预测
+```shell
+python -m paddlerec.run -m paddlerec.models.match.dssm # dssm
+python -m paddlerec.run -m paddlerec.models.match.multiview-simnet # multiview-simnet
+```
+
diff --git a/models/multitask/__init__.py b/models/multitask/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/models/multitask/esmm/__init__.py b/models/multitask/esmm/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/multitask/esmm/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/multitask/esmm/config.yaml b/models/multitask/esmm/config.yaml
new file mode 100644
index 000000000..f40b967c1
--- /dev/null
+++ b/models/multitask/esmm/config.yaml
@@ -0,0 +1,51 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+evaluate:
+ reader:
+ batch_size: 1
+ class: "{workspace}/esmm_infer_reader.py"
+ test_data_path: "{workspace}/data/train"
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 3
+ workspace: "paddlerec.models.multitask.esmm"
+ device: cpu
+
+ reader:
+ batch_size: 2
+ class: "{workspace}/esmm_reader.py"
+ train_data_path: "{workspace}/data/train"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ vocab_size: 10000
+ embed_size: 128
+ learning_rate: 0.001
+ optimizer: adam
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/multitask/esmm/data/test/small_test.txt b/models/multitask/esmm/data/test/small_test.txt
new file mode 100644
index 000000000..b4bf71896
--- /dev/null
+++ b/models/multitask/esmm/data/test/small_test.txt
@@ -0,0 +1,100 @@
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+430 445 433
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+0 0
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+462 150 150
+471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
diff --git a/models/multitask/esmm/data/train/small.csv b/models/multitask/esmm/data/train/small.csv
new file mode 100644
index 000000000..f5febad64
--- /dev/null
+++ b/models/multitask/esmm/data/train/small.csv
@@ -0,0 +1,10 @@
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diff --git a/models/multitask/esmm/esmm_infer_reader.py b/models/multitask/esmm/esmm_infer_reader.py
new file mode 100644
index 000000000..8ca9eca67
--- /dev/null
+++ b/models/multitask/esmm/esmm_infer_reader.py
@@ -0,0 +1,64 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+from collections import defaultdict
+
+from paddlerec.core.reader import Reader
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
+ '129',
+ '205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
+ self.all_field_id_dict = defaultdict(int)
+ for i, field_id in enumerate(all_field_id):
+ self.all_field_id_dict[field_id] = [False, i]
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.strip().split(',')
+ ctr = int(features[1])
+ cvr = int(features[2])
+
+ padding = 0
+ output = [(field_id, []) for field_id in self.all_field_id_dict]
+
+ for elem in features[4:]:
+ field_id, feat_id = elem.strip().split(':')
+ if field_id not in self.all_field_id_dict:
+ continue
+ self.all_field_id_dict[field_id][0] = True
+ index = self.all_field_id_dict[field_id][1]
+ output[index][1].append(int(feat_id))
+
+ for field_id in self.all_field_id_dict:
+ visited, index = self.all_field_id_dict[field_id]
+ if visited:
+ self.all_field_id_dict[field_id][0] = False
+ else:
+ output[index][1].append(padding)
+ output.append(('ctr', [ctr]))
+ output.append(('cvr', [cvr]))
+ yield output
+
+ return reader
diff --git a/models/multitask/esmm/esmm_reader.py b/models/multitask/esmm/esmm_reader.py
new file mode 100644
index 000000000..3d663038e
--- /dev/null
+++ b/models/multitask/esmm/esmm_reader.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from collections import defaultdict
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
+ '129',
+ '205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
+ self.all_field_id_dict = defaultdict(int)
+ for i, field_id in enumerate(all_field_id):
+ self.all_field_id_dict[field_id] = [False, i]
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ features = line.strip().split(',')
+ # ctr = list(map(int, features[1]))
+ # cvr = list(map(int, features[2]))
+ ctr = int(features[1])
+ cvr = int(features[2])
+
+ padding = 0
+ output = [(field_id, []) for field_id in self.all_field_id_dict]
+
+ for elem in features[4:]:
+ field_id, feat_id = elem.strip().split(':')
+ if field_id not in self.all_field_id_dict:
+ continue
+ self.all_field_id_dict[field_id][0] = True
+ index = self.all_field_id_dict[field_id][1]
+ # feat_id = list(map(int, feat_id))
+ output[index][1].append(int(feat_id))
+
+ for field_id in self.all_field_id_dict:
+ visited, index = self.all_field_id_dict[field_id]
+ if visited:
+ self.all_field_id_dict[field_id][0] = False
+ else:
+ output[index][1].append(padding)
+ output.append(('ctr', [ctr]))
+ output.append(('cvr', [cvr]))
+ yield output
+
+ return reader
diff --git a/models/multitask/esmm/model.py b/models/multitask/esmm/model.py
new file mode 100644
index 000000000..8a8a203a8
--- /dev/null
+++ b/models/multitask/esmm/model.py
@@ -0,0 +1,121 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def fc(self, tag, data, out_dim, active='prelu'):
+
+ init_stddev = 1.0
+ scales = 1.0 / np.sqrt(data.shape[1])
+
+ p_attr = fluid.param_attr.ParamAttr(name='%s_weight' % tag,
+ initializer=fluid.initializer.NormalInitializer(loc=0.0,
+ scale=init_stddev * scales))
+
+ b_attr = fluid.ParamAttr(name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
+
+ out = fluid.layers.fc(input=data,
+ size=out_dim,
+ act=active,
+ param_attr=p_attr,
+ bias_attr=b_attr,
+ name=tag)
+ return out
+
+ def input_data(self):
+ sparse_input_ids = [
+ fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0, 23)
+ ]
+ label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
+ label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
+ inputs = sparse_input_ids + [label_ctr] + [label_cvr]
+ self._data_var.extend(inputs)
+
+ return inputs
+
+ def net(self, inputs, is_infer=False):
+
+ vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
+ embed_size = envs.get_global_env("hyper_parameters.embed_size", None, self._namespace)
+ emb = []
+ for data in inputs[0:-2]:
+ feat_emb = fluid.embedding(input=data,
+ size=[vocab_size, embed_size],
+ param_attr=fluid.ParamAttr(name='dis_emb',
+ learning_rate=5,
+ initializer=fluid.initializer.Xavier(
+ fan_in=embed_size, fan_out=embed_size)
+ ),
+ is_sparse=True)
+ field_emb = fluid.layers.sequence_pool(input=feat_emb, pool_type='sum')
+ emb.append(field_emb)
+ concat_emb = fluid.layers.concat(emb, axis=1)
+
+ # ctr
+ active = 'relu'
+ ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
+ ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
+ ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax')
+
+ # cvr
+ cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
+ cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
+ cvr_out = self.fc('cvr_out', cvr_fc2, 2, 'softmax')
+
+ ctr_clk = inputs[-2]
+ ctcvr_buy = inputs[-1]
+
+ ctr_prop_one = fluid.layers.slice(ctr_out, axes=[1], starts=[1], ends=[2])
+ cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
+
+ ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
+ ctcvr_prop = fluid.layers.concat(input=[1 - ctcvr_prop_one, ctcvr_prop_one], axis=1)
+
+ auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
+ auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
+
+ if is_infer:
+ self._infer_results["AUC_ctr"] = auc_ctr
+ self._infer_results["AUC_ctcvr"] = auc_ctcvr
+ return
+
+ loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
+ loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
+ cost = loss_ctr + loss_ctcvr
+ avg_cost = fluid.layers.mean(cost)
+
+ self._cost = avg_cost
+ self._metrics["AUC_ctr"] = auc_ctr
+ self._metrics["BATCH_AUC_ctr"] = batch_auc_ctr
+ self._metrics["AUC_ctcvr"] = auc_ctcvr
+ self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
+
+ def train_net(self):
+ input_data = self.input_data()
+ self.net(input_data)
+
+ def infer_net(self):
+ self._infer_data_var = self.input_data()
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+ self.net(self._infer_data_var, is_infer=True)
diff --git a/models/multitask/mmoe/__init__.py b/models/multitask/mmoe/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/multitask/mmoe/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/multitask/mmoe/census_infer_reader.py b/models/multitask/mmoe/census_infer_reader.py
new file mode 100644
index 000000000..c62de8e69
--- /dev/null
+++ b/models/multitask/mmoe/census_infer_reader.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ l = line.strip().split(',')
+ l = list(map(float, l))
+ label_income = []
+ label_marital = []
+ data = l[2:]
+ if int(l[1]) == 0:
+ label_income = [1, 0]
+ elif int(l[1]) == 1:
+ label_income = [0, 1]
+ if int(l[0]) == 0:
+ label_marital = [1, 0]
+ elif int(l[0]) == 1:
+ label_marital = [0, 1]
+ feature_name = ["input", "label_income", "label_marital"]
+ yield zip(feature_name, [data] + [label_income] + [label_marital])
+
+ return reader
diff --git a/models/multitask/mmoe/census_reader.py b/models/multitask/mmoe/census_reader.py
new file mode 100644
index 000000000..211e56688
--- /dev/null
+++ b/models/multitask/mmoe/census_reader.py
@@ -0,0 +1,51 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ l = line.strip().split(',')
+ l = list(map(float, l))
+ label_income = []
+ label_marital = []
+ data = l[2:]
+ if int(l[1]) == 0:
+ label_income = [1, 0]
+ elif int(l[1]) == 1:
+ label_income = [0, 1]
+ if int(l[0]) == 0:
+ label_marital = [1, 0]
+ elif int(l[0]) == 1:
+ label_marital = [0, 1]
+ # label_income = np.array(label_income)
+ # label_marital = np.array(label_marital)
+ feature_name = ["input", "label_income", "label_marital"]
+ yield zip(feature_name, [data] + [label_income] + [label_marital])
+
+ return reader
diff --git a/models/multitask/mmoe/config.yaml b/models/multitask/mmoe/config.yaml
new file mode 100644
index 000000000..e23332cda
--- /dev/null
+++ b/models/multitask/mmoe/config.yaml
@@ -0,0 +1,54 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+evaluate:
+ reader:
+ batch_size: 1
+ class: "{workspace}/census_infer_reader.py"
+ test_data_path: "{workspace}/data/train"
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 3
+ workspace: "paddlerec.models.multitask.mmoe"
+ device: cpu
+
+ reader:
+ batch_size: 1
+ class: "{workspace}/census_reader.py"
+ train_data_path: "{workspace}/data/train"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ feature_size: 499
+ expert_num: 8
+ gate_num: 2
+ expert_size: 16
+ tower_size: 8
+ learning_rate: 0.001
+ optimizer: adam
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/multitask/mmoe/data/test/small_test.txt b/models/multitask/mmoe/data/test/small_test.txt
new file mode 100644
index 000000000..b4bf71896
--- /dev/null
+++ b/models/multitask/mmoe/data/test/small_test.txt
@@ -0,0 +1,100 @@
+0 16
+475 473 155
+491 21
+96 185 96
+29 14 13
+5 481 11 21 470
+70 5 70 11
+167 42 167 217
+72 15 73 161 172
+82 82
+97 297 97
+193 182 186 183 184 177 214
+152 152
+163 298 7
+39 73 71
+490 23 23 496 488 74 23 74 486 23 23 74
+17 17
+170 170 483 444 443 234
+25 472
+5 5 11 70 69
+149 149 455
+356 68 477 468 17 479 66
+159 172 6 71 6 6 158 13 494 169
+155 44 438 144 500
+156 9 9
+146 146
+173 10 10 461
+7 6 6
+269 48 268
+50 100
+323 174 18
+69 69 22 98
+38 171
+22 29 489 10
+0 0
+11 5
+29 13 14 232 231 451 289 452 229
+260 11 156
+166 160 166 39
+223 134 134 420
+66 401 68 132 17 84 287 5
+39 304
+65 84 132
+400 211
+145 144
+16 28 254 48 50 100 42 154 262 133 17
+0 0
+28 28
+11 476 464
+61 61 86 86
+38 38
+463 478
+437 265
+22 39 485 171 98
+434 51 344
+16 16
+67 67 67 448
+22 12 161
+15 377 147 147 374
+119 317 0
+38 484
+403 499
+432 442
+28 0 16 50 465 42
+163 487 7 162
+99 99 325 423 83 83
+154 133
+5 37 492 235 160 279
+10 10 457 493 10 460
+441 4 4 4 4 4 4 4
+153 153
+159 164 164
+328 37
+65 65 404 347 431 459
+80 80 44 44
+61 446
+162 495 7 453
+157 21 204 68 37 66 469 145
+37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
+430 445 433
+449 14
+9 9 9 9
+440 238 226
+148 148
+266 267 181
+48 498
+263 255 256
+458 158 7
+72 168 12 165 71 73 173 49
+0 0
+7 7 6
+14 29 13 6 15 14 15 13
+480 439 21
+450 21 151
+12 12 49 14 13 165 12 169 72 15 15
+91 91
+22 12 49 168
+497 101 30 411 30 482 30 53 30 101 176 415 53 447
+462 150 150
+471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
diff --git a/models/multitask/mmoe/data/train/train_data.txt b/models/multitask/mmoe/data/train/train_data.txt
new file mode 100644
index 000000000..992314e44
--- /dev/null
+++ b/models/multitask/mmoe/data/train/train_data.txt
@@ -0,0 +1,10 @@
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diff --git a/models/multitask/mmoe/model.py b/models/multitask/mmoe/model.py
new file mode 100644
index 000000000..525e9d5cc
--- /dev/null
+++ b/models/multitask/mmoe/model.py
@@ -0,0 +1,112 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def MMOE(self, is_infer=False):
+
+ feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
+ expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self._namespace)
+ gate_num = envs.get_global_env("hyper_parameters.gate_num", None, self._namespace)
+ expert_size = envs.get_global_env("hyper_parameters.expert_size", None, self._namespace)
+ tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
+
+ input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
+ label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
+ label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
+ if is_infer:
+ self._infer_data_var = [input_data, label_income, label_marital]
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+ self._data_var.extend([input_data, label_income, label_marital])
+ # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
+ expert_outputs = []
+ for i in range(0, expert_num):
+ expert_output = fluid.layers.fc(input=input_data,
+ size=expert_size,
+ act='relu',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='expert_' + str(i))
+ expert_outputs.append(expert_output)
+ expert_concat = fluid.layers.concat(expert_outputs, axis=1)
+ expert_concat = fluid.layers.reshape(expert_concat, [-1, expert_num, expert_size])
+
+ # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
+ output_layers = []
+ for i in range(0, gate_num):
+ cur_gate = fluid.layers.fc(input=input_data,
+ size=expert_num,
+ act='softmax',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='gate_' + str(i))
+ # f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
+ cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0)
+ cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1)
+ # Build tower layer
+ cur_tower = fluid.layers.fc(input=cur_gate_expert,
+ size=tower_size,
+ act='relu',
+ name='task_layer_' + str(i))
+ out = fluid.layers.fc(input=cur_tower,
+ size=2,
+ act='softmax',
+ name='out_' + str(i))
+
+ output_layers.append(out)
+
+ pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
+ pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
+
+ label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
+ label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
+
+ auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
+ label=fluid.layers.cast(x=label_income_1,
+ dtype='int64'))
+ auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
+ label=fluid.layers.cast(x=label_marital_1,
+ dtype='int64'))
+ if is_infer:
+ self._infer_results["AUC_income"] = auc_income
+ self._infer_results["AUC_marital"] = auc_marital
+ return
+
+ cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
+ cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
+
+ avg_cost_income = fluid.layers.mean(x=cost_income)
+ avg_cost_marital = fluid.layers.mean(x=cost_marital)
+
+ cost = avg_cost_income + avg_cost_marital
+
+ self._cost = cost
+ self._metrics["AUC_income"] = auc_income
+ self._metrics["BATCH_AUC_income"] = batch_auc_1
+ self._metrics["AUC_marital"] = auc_marital
+ self._metrics["BATCH_AUC_marital"] = batch_auc_2
+
+ def train_net(self):
+ self.MMOE()
+
+ def infer_net(self):
+ self.MMOE(is_infer=True)
diff --git a/models/multitask/readme.md b/models/multitask/readme.md
new file mode 100755
index 000000000..d234f42f1
--- /dev/null
+++ b/models/multitask/readme.md
@@ -0,0 +1,59 @@
+# 多任务学习模型库
+
+## 简介
+我们提供了常见的多任务学习中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的多任务模型包括 [MMoE](mmoe)、[Share-Bottom](share-bottom)、[ESMM](esmm)。
+
+模型算法库在持续添加中，欢迎关注。
+
+## 目录
+* [整体介绍](#整体介绍)
+ * [多任务模型列表](#多任务模型列表)
+* [使用教程](#使用教程)
+ * [训练&预测](#训练&预测)
+* [效果对比](#效果对比)
+ * [模型效果列表](#模型效果列表)
+
+## 整体介绍
+### 多任务模型列表
+
+| 模型 | 简介 | 论文 |
+| :------------------: | :--------------------: | :---------: |
+| Share-Bottom | share-bottom | [Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)(1998) |
+| ESMM | Entire Space Multi-Task Model | [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)(2018) |
+| MMoE | Multi-gate Mixture-of-Experts | [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)(2018) |
+
+下面是每个模型的简介（注：图片引用自链接中的论文）
+
+
+[ESMM](https://arxiv.org/abs/1804.07931):
+
+
+
+
+[Share-Bottom](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf):
+
+
+
+
+[MMoE](https://dl.acm.org/doi/abs/10.1145/3219819.3220007):
+
+
+
+
+## 使用教程
+### 训练&预测
+```shell
+python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe
+python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom
+python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm
+```
+
+## 效果对比
+### 模型效果列表
+
+| 数据集 | 模型 | loss | auc |
+| :------------------: | :--------------------: | :---------: |:---------: |
+| Census-income Data | Share-Bottom | -- | 0.93120/0.99256 |
+| Census-income Data | MMoE | -- | 0.94465/0.99324 |
+| Ali-CCP | ESMM | -- | 0.97181/0.49967 |
+
diff --git a/models/multitask/share-bottom/__init__.py b/models/multitask/share-bottom/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/multitask/share-bottom/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/multitask/share-bottom/census_infer_reader.py b/models/multitask/share-bottom/census_infer_reader.py
new file mode 100644
index 000000000..c62de8e69
--- /dev/null
+++ b/models/multitask/share-bottom/census_infer_reader.py
@@ -0,0 +1,49 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class EvaluateReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ l = line.strip().split(',')
+ l = list(map(float, l))
+ label_income = []
+ label_marital = []
+ data = l[2:]
+ if int(l[1]) == 0:
+ label_income = [1, 0]
+ elif int(l[1]) == 1:
+ label_income = [0, 1]
+ if int(l[0]) == 0:
+ label_marital = [1, 0]
+ elif int(l[0]) == 1:
+ label_marital = [0, 1]
+ feature_name = ["input", "label_income", "label_marital"]
+ yield zip(feature_name, [data] + [label_income] + [label_marital])
+
+ return reader
diff --git a/models/multitask/share-bottom/census_reader.py b/models/multitask/share-bottom/census_reader.py
new file mode 100644
index 000000000..211e56688
--- /dev/null
+++ b/models/multitask/share-bottom/census_reader.py
@@ -0,0 +1,51 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from paddlerec.core.reader import Reader
+
+
+class TrainReader(Reader):
+ def init(self):
+ pass
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def reader():
+ """
+ This function needs to be implemented by the user, based on data format
+ """
+ l = line.strip().split(',')
+ l = list(map(float, l))
+ label_income = []
+ label_marital = []
+ data = l[2:]
+ if int(l[1]) == 0:
+ label_income = [1, 0]
+ elif int(l[1]) == 1:
+ label_income = [0, 1]
+ if int(l[0]) == 0:
+ label_marital = [1, 0]
+ elif int(l[0]) == 1:
+ label_marital = [0, 1]
+ # label_income = np.array(label_income)
+ # label_marital = np.array(label_marital)
+ feature_name = ["input", "label_income", "label_marital"]
+ yield zip(feature_name, [data] + [label_income] + [label_marital])
+
+ return reader
diff --git a/models/multitask/share-bottom/config.yaml b/models/multitask/share-bottom/config.yaml
new file mode 100644
index 000000000..591b6800c
--- /dev/null
+++ b/models/multitask/share-bottom/config.yaml
@@ -0,0 +1,53 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+evaluate:
+ reader:
+ batch_size: 1
+ class: "{workspace}/census_infer_reader.py"
+ test_data_path: "{workspace}/data/train"
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 3
+ workspace: "paddlerec.models.multitask.share-bottom"
+ device: cpu
+
+ reader:
+ batch_size: 2
+ class: "{workspace}/census_reader.py"
+ train_data_path: "{workspace}/data/train"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ feature_size: 499
+ bottom_size: 117
+ tower_nums: 2
+ tower_size: 8
+ learning_rate: 0.001
+ optimizer: adam
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/multitask/share-bottom/data/test/small_test.txt b/models/multitask/share-bottom/data/test/small_test.txt
new file mode 100644
index 000000000..b4bf71896
--- /dev/null
+++ b/models/multitask/share-bottom/data/test/small_test.txt
@@ -0,0 +1,100 @@
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+430 445 433
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+0 0
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+91 91
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+462 150 150
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diff --git a/models/multitask/share-bottom/data/train/train_data.txt b/models/multitask/share-bottom/data/train/train_data.txt
new file mode 100644
index 000000000..992314e44
--- /dev/null
+++ b/models/multitask/share-bottom/data/train/train_data.txt
@@ -0,0 +1,10 @@
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diff --git a/models/multitask/share-bottom/model.py b/models/multitask/share-bottom/model.py
new file mode 100644
index 000000000..d570ba770
--- /dev/null
+++ b/models/multitask/share-bottom/model.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def model(self, is_infer=False):
+
+ feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
+ bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None, self._namespace)
+ tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
+ tower_nums = envs.get_global_env("hyper_parameters.tower_nums", None, self._namespace)
+
+ input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
+ label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
+ label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
+
+ if is_infer:
+ self._infer_data_var = [input_data, label_income, label_marital]
+ self._infer_data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+ self._data_var.extend([input_data, label_income, label_marital])
+
+ bottom_output = fluid.layers.fc(input=input_data,
+ size=bottom_size,
+ act='relu',
+ bias_attr=fluid.ParamAttr(learning_rate=1.0),
+ name='bottom_output')
+
+ # Build tower layer from bottom layer
+ output_layers = []
+ for index in range(tower_nums):
+ tower_layer = fluid.layers.fc(input=bottom_output,
+ size=tower_size,
+ act='relu',
+ name='task_layer_' + str(index))
+ output_layer = fluid.layers.fc(input=tower_layer,
+ size=2,
+ act='softmax',
+ name='output_layer_' + str(index))
+ output_layers.append(output_layer)
+
+ pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
+ pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
+
+ label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
+ label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
+
+ auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
+ label=fluid.layers.cast(x=label_income_1,
+ dtype='int64'))
+ auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
+ label=fluid.layers.cast(x=label_marital_1,
+ dtype='int64'))
+
+ if is_infer:
+ self._infer_results["AUC_income"] = auc_income
+ self._infer_results["AUC_marital"] = auc_marital
+ return
+
+ cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
+ cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
+ cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
+
+ avg_cost = fluid.layers.mean(x=cost)
+
+ self._cost = avg_cost
+ self._metrics["AUC_income"] = auc_income
+ self._metrics["BATCH_AUC_income"] = batch_auc_1
+ self._metrics["AUC_marital"] = auc_marital
+ self._metrics["BATCH_AUC_marital"] = batch_auc_2
+
+ def train_net(self):
+ self.model()
+
+ def infer_net(self):
+ self.model(is_infer=True)
diff --git a/models/rank/__init__.py b/models/rank/__init__.py
new file mode 100755
index 000000000..e69de29bb
diff --git a/models/rank/dcn/__init__.py b/models/rank/dcn/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/rank/dcn/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rank/dcn/config.yaml b/models/rank/dcn/config.yaml
new file mode 100755
index 000000000..6cd89d6aa
--- /dev/null
+++ b/models/rank/dcn/config.yaml
@@ -0,0 +1,54 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 10
+ workspace: "paddlerec.models.rank.dcn"
+
+ reader:
+ batch_size: 2
+ train_data_path: "{workspace}/data/slot_train"
+ feat_dict_name: "{workspace}/data/vocab"
+ sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
+ dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ cross_num: 2
+ dnn_hidden_units: [128, 128]
+ l2_reg_cross: 0.00005
+ dnn_use_bn: False
+ clip_by_norm: 100.0
+ cat_feat_num: "{workspace}/data/cat_feature_num.txt"
+ is_sparse: False
+ is_test: False
+ num_field: 39
+ learning_rate: 0.0001
+ act: "relu"
+ optimizer: adam
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/rank/dcn/data/download.py b/models/rank/dcn/data/download.py
new file mode 100755
index 000000000..d9bcc6df2
--- /dev/null
+++ b/models/rank/dcn/data/download.py
@@ -0,0 +1,24 @@
+import os
+import sys
+import io
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from paddlerec.tools.tools import download_file_and_uncompress
+
+if __name__ == '__main__':
+ trainfile = 'train.txt'
+ url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
+
+ print("download and extract starting...")
+ download_file_and_uncompress(url)
+ print("download and extract finished")
+
+ count = 0
+ for _ in io.open(trainfile, 'r', encoding='utf-8'):
+ count += 1
+
+ print("total records: %d" % count)
+ print("done")
diff --git a/models/rank/dcn/data/get_slot_data.py b/models/rank/dcn/data/get_slot_data.py
new file mode 100755
index 000000000..77b30296a
--- /dev/null
+++ b/models/rank/dcn/data/get_slot_data.py
@@ -0,0 +1,114 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+import sys
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+import math
+import os
+try:
+ import cPickle as pickle
+except ImportError:
+ import pickle
+from collections import Counter
+import os
+import paddle.fluid.incubate.data_generator as dg
+
+class TrainReader(dg.MultiSlotDataGenerator):
+
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
+ def init(self):
+ self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+ self.cont_max_ = [
+ 5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 11,
+ 231, 4008, 7393
+ ]
+ self.cont_diff_ = [
+ self.cont_max_[i] - self.cont_min_[i]
+ for i in range(len(self.cont_min_))
+ ]
+ self.cont_idx_ = list(range(1, 14))
+ self.cat_idx_ = list(range(14, 40))
+
+ dense_feat_names = ['I' + str(i) for i in range(1, 14)]
+ sparse_feat_names = ['C' + str(i) for i in range(1, 27)]
+ target = ['label']
+
+ self.label_feat_names = target + dense_feat_names + sparse_feat_names
+
+ self.cat_feat_idx_dict_list = [{} for _ in range(26)]
+
+ # TODO: set vocabulary dictionary
+ vocab_dir = "./vocab/"
+ for i in range(26):
+ lookup_idx = 1 # remain 0 for default value
+ for line in open(
+ os.path.join(vocab_dir, 'C' + str(i + 1) + '.txt')):
+ self.cat_feat_idx_dict_list[i][line.strip()] = lookup_idx
+ lookup_idx += 1
+
+ def _process_line(self, line):
+ features = line.rstrip('\n').split('\t')
+ label_feat_list = [[] for _ in range(40)]
+ for idx in self.cont_idx_:
+ if features[idx] == '':
+ label_feat_list[idx].append(0)
+ else:
+ # 0-1 minmax norm
+ # label_feat_list[idx].append((float(features[idx]) - self.cont_min_[idx - 1]) /
+ # self.cont_diff_[idx - 1])
+ # log transform
+ label_feat_list[idx].append(
+ math.log(4 + float(features[idx]))
+ if idx == 2 else math.log(1 + float(features[idx])))
+ for idx in self.cat_idx_:
+ if features[idx] == '' or features[
+ idx] not in self.cat_feat_idx_dict_list[idx - 14]:
+ label_feat_list[idx].append(0)
+ else:
+ label_feat_list[idx].append(self.cat_feat_idx_dict_list[
+ idx - 14][features[idx]])
+ label_feat_list[0].append(int(features[0]))
+ return label_feat_list
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def data_iter():
+ label_feat_list = self._process_line(line)
+ s = ""
+ for i in list(zip(self.label_feat_names, label_feat_list)):
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+
+ return data_iter
+
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/dcn/data/preprocess.py b/models/rank/dcn/data/preprocess.py
new file mode 100755
index 000000000..b35660772
--- /dev/null
+++ b/models/rank/dcn/data/preprocess.py
@@ -0,0 +1,131 @@
+from __future__ import print_function, absolute_import, division
+
+import os
+import sys
+from collections import Counter
+import numpy as np
+"""
+preprocess Criteo train data, generate extra statistic files for model input.
+"""
+# input filename
+FILENAME = 'train.200000.txt'
+
+# global vars
+CAT_FEATURE_NUM = 'cat_feature_num.txt'
+INT_FEATURE_MINMAX = 'int_feature_minmax.txt'
+VOCAB_DIR = 'vocab'
+TRAIN_DIR = 'train'
+TEST_VALID_DIR = 'test_valid'
+SPLIT_RATIO = 0.9
+FREQ_THR = 10
+
+INT_COLUMN_NAMES = ['I' + str(i) for i in range(1, 14)]
+CAT_COLUMN_NAMES = ['C' + str(i) for i in range(1, 27)]
+
+
+def check_statfiles():
+ """
+ check if statistic files of Criteo exists
+ :return:
+ """
+ statsfiles = [CAT_FEATURE_NUM, INT_FEATURE_MINMAX] + [
+ os.path.join(VOCAB_DIR, cat_fn + '.txt') for cat_fn in CAT_COLUMN_NAMES
+ ]
+ if all([os.path.exists(fn) for fn in statsfiles]):
+ return True
+ return False
+
+
+def create_statfiles():
+ """
+ create statistic files of Criteo, including:
+ min/max of interger features
+ counts of categorical features
+ vocabs of each categorical features
+ :return:
+ """
+ int_minmax_list = [[sys.maxsize, -sys.maxsize]
+ for _ in range(13)] # count integer feature min max
+ cat_ct_list = [Counter() for _ in range(26)] # count categorical features
+ for idx, line in enumerate(open(FILENAME)):
+ spls = line.rstrip('\n').split('\t')
+ assert len(spls) == 40
+
+ for i in range(13):
+ if not spls[1 + i]: continue
+ int_val = int(spls[1 + i])
+ int_minmax_list[i][0] = min(int_minmax_list[i][0], int_val)
+ int_minmax_list[i][1] = max(int_minmax_list[i][1], int_val)
+
+ for i in range(26):
+ cat_ct_list[i].update([spls[14 + i]])
+
+ # save min max of integer features
+ with open(INT_FEATURE_MINMAX, 'w') as f:
+ for name, minmax in zip(INT_COLUMN_NAMES, int_minmax_list):
+ print("{} {} {}".format(name, minmax[0], minmax[1]), file=f)
+
+ # remove '' from all cat_set[i] and filter low freq categorical value
+ cat_set_list = [set() for i in range(len(cat_ct_list))]
+ for i, ct in enumerate(cat_ct_list):
+ if '' in ct: del ct['']
+ for key in list(ct.keys()):
+ if ct[key] >= FREQ_THR:
+ cat_set_list[i].add(key)
+
+ del cat_ct_list
+
+ # create vocab dir
+ if not os.path.exists(VOCAB_DIR):
+ os.makedirs(VOCAB_DIR)
+
+ # write vocab file of categorical features
+ with open(CAT_FEATURE_NUM, 'w') as cat_feat_count_file:
+ for name, s in zip(CAT_COLUMN_NAMES, cat_set_list):
+ print('{} {}'.format(name, len(s)), file=cat_feat_count_file)
+
+ vocabfile = os.path.join(VOCAB_DIR, name + '.txt')
+
+ with open(vocabfile, 'w') as f:
+ for vocab_val in s:
+ print(vocab_val, file=f)
+
+
+def split_data():
+ """
+ split train.txt into train and test_valid files.
+ :return:
+ """
+ if not os.path.exists(TRAIN_DIR):
+ os.makedirs(TRAIN_DIR)
+ if not os.path.exists(TEST_VALID_DIR):
+ os.makedirs(TEST_VALID_DIR)
+
+ fin = open('train.200000.txt', 'r')
+ data_dir = TRAIN_DIR
+ fout = open(os.path.join(data_dir, 'part-0'), 'w')
+ split_idx = int(45840617 * SPLIT_RATIO)
+ for line_idx, line in enumerate(fin):
+ if line_idx == split_idx:
+ fout.close()
+ data_dir = TEST_VALID_DIR
+ cur_part_idx = int(line_idx / 200000)
+ fout = open(
+ os.path.join(data_dir, 'part-' + str(cur_part_idx)), 'w')
+ if line_idx % 200000 == 0 and line_idx != 0:
+ fout.close()
+ cur_part_idx = int(line_idx / 200000)
+ fout = open(
+ os.path.join(data_dir, 'part-' + str(cur_part_idx)), 'w')
+ fout.write(line)
+ fout.close()
+ fin.close()
+
+
+if __name__ == '__main__':
+ if not check_statfiles():
+ print('create statstic files of Criteo...')
+ create_statfiles()
+ print('split train.200000.txt...')
+ split_data()
+ print('done')
diff --git a/models/rank/dcn/data/run.sh b/models/rank/dcn/data/run.sh
new file mode 100644
index 000000000..32d653dc4
--- /dev/null
+++ b/models/rank/dcn/data/run.sh
@@ -0,0 +1,14 @@
+python download.py
+python preprocess.py
+
+mkdir slot_train
+for i in `ls ./train`
+do
+ cat train/$i | python get_slot_data.py > slot_train/$i
+done
+
+mkdir slot_test_valid
+for i in `ls ./test_valid`
+do
+ cat test_valid/$i | python get_slot_data.py > slot_test_valid/$i
+done
diff --git a/models/rank/dcn/model.py b/models/rank/dcn/model.py
new file mode 100755
index 000000000..bf3e3051c
--- /dev/null
+++ b/models/rank/dcn/model.py
@@ -0,0 +1,154 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def init_network(self):
+ self.cross_num = envs.get_global_env("hyper_parameters.cross_num", None, self._namespace)
+ self.dnn_hidden_units = envs.get_global_env("hyper_parameters.dnn_hidden_units", None, self._namespace)
+ self.l2_reg_cross = envs.get_global_env("hyper_parameters.l2_reg_cross", None, self._namespace)
+ self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn", None, self._namespace)
+ self.clip_by_norm = envs.get_global_env("hyper_parameters.clip_by_norm", None, self._namespace)
+ cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num", None, self._namespace)
+
+ self.sparse_inputs = self._sparse_data_var[1:]
+ self.dense_inputs = self._dense_data_var
+ self.target_input = self._sparse_data_var[0]
+
+ cat_feat_dims_dict = OrderedDict()
+ for line in open(cat_feat_num):
+ spls = line.strip().split()
+ assert len(spls) == 2
+ cat_feat_dims_dict[spls[0]] = int(spls[1])
+ self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
+ )
+ self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", None, self._namespace)
+
+ self.dense_feat_names = [i.name for i in self.dense_inputs]
+ self.sparse_feat_names = [i.name for i in self.sparse_inputs]
+
+ # {feat_name: dims}
+ self.feat_dims_dict = OrderedDict(
+ [(feat_name, 1) for feat_name in self.dense_feat_names])
+ self.feat_dims_dict.update(self.cat_feat_dims_dict)
+
+ self.net_input = None
+ self.loss = None
+
+ def _create_embedding_input(self):
+ # sparse embedding
+ sparse_emb_dict = OrderedDict()
+ for var in self.sparse_inputs:
+ sparse_emb_dict[var.name] = fluid.embedding(input=var,
+ size=[self.feat_dims_dict[var.name] + 1,
+ 6 * int(pow(self.feat_dims_dict[var.name], 0.25))
+ ],is_sparse=self.is_sparse)
+
+ # combine dense and sparse_emb
+ dense_input_list = self.dense_inputs
+ sparse_emb_list = list(sparse_emb_dict.values())
+
+ sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1)
+ sparse_input = fluid.layers.flatten(sparse_input)
+
+ dense_input = fluid.layers.concat(dense_input_list, axis=-1)
+ dense_input = fluid.layers.flatten(dense_input)
+ dense_input = fluid.layers.cast(dense_input, 'float32')
+
+ net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1)
+
+ return net_input
+
+ def _deep_net(self, input, hidden_units, use_bn=False, is_test=False):
+ for units in hidden_units:
+ input = fluid.layers.fc(input=input, size=units)
+ if use_bn:
+ input = fluid.layers.batch_norm(input, is_test=is_test)
+ input = fluid.layers.relu(input)
+ return input
+
+ def _cross_layer(self, x0, x, prefix):
+ input_dim = x0.shape[-1]
+ w = fluid.layers.create_parameter(
+ [input_dim], dtype='float32', name=prefix + "_w")
+ b = fluid.layers.create_parameter(
+ [input_dim], dtype='float32', name=prefix + "_b")
+ xw = fluid.layers.reduce_sum(x * w, dim=1, keep_dim=True) # (N, 1)
+ return x0 * xw + b + x, w
+
+ def _cross_net(self, input, num_corss_layers):
+ x = x0 = input
+ l2_reg_cross_list = []
+ for i in range(num_corss_layers):
+ x, w = self._cross_layer(x0, x, "cross_layer_{}".format(i))
+ l2_reg_cross_list.append(self._l2_loss(w))
+ l2_reg_cross_loss = fluid.layers.reduce_sum(
+ fluid.layers.concat(
+ l2_reg_cross_list, axis=-1))
+ return x, l2_reg_cross_loss
+
+ def _l2_loss(self, w):
+ return fluid.layers.reduce_sum(fluid.layers.square(w))
+
+ def train_net(self):
+ self.model._init_slots()
+ self.init_network()
+
+ self.net_input = self._create_embedding_input()
+
+ deep_out = self._deep_net(self.net_input, self.dnn_hidden_units, self.dnn_use_bn, False)
+
+ cross_out, l2_reg_cross_loss = self._cross_net(self.net_input,
+ self.cross_num)
+
+ last_out = fluid.layers.concat([deep_out, cross_out], axis=-1)
+ logit = fluid.layers.fc(last_out, 1)
+
+ self.prob = fluid.layers.sigmoid(logit)
+
+ # auc
+ prob_2d = fluid.layers.concat([1 - self.prob, self.prob], 1)
+ label_int = fluid.layers.cast(self.target_input, 'int64')
+ auc_var, batch_auc_var, self.auc_states = fluid.layers.auc(
+ input=prob_2d, label=label_int, slide_steps=0)
+ self._metrics["AUC"] = auc_var
+ self._metrics["BATCH_AUC"] = batch_auc_var
+
+ # logloss
+ logloss = fluid.layers.log_loss(self.prob, fluid.layers.cast(self.target_input, dtype='float32'))
+ self.avg_logloss = fluid.layers.reduce_mean(logloss)
+
+ # reg_coeff * l2_reg_cross
+ l2_reg_cross_loss = self.l2_reg_cross * l2_reg_cross_loss
+ self.loss = self.avg_logloss + l2_reg_cross_loss
+ self._cost = self.loss
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
+ return optimizer
+
+ def infer_net(self, parameter_list):
+ self.model._init_slots()
+ self.deepfm_net()
diff --git a/models/rank/deepfm/__init__.py b/models/rank/deepfm/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/rank/deepfm/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rank/deepfm/config.yaml b/models/rank/deepfm/config.yaml
new file mode 100755
index 000000000..21c6039ca
--- /dev/null
+++ b/models/rank/deepfm/config.yaml
@@ -0,0 +1,50 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 10
+ workspace: "paddlerec.models.rank.deepfm"
+
+ reader:
+ batch_size: 2
+ train_data_path: "{workspace}/data/slot_train_data"
+ feat_dict_name: "{workspace}/data/feat_dict_10.pkl2"
+ sparse_slots: "label feat_idx"
+ dense_slots: "feat_value:39"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ sparse_feature_number: 1086460
+ sparse_feature_dim: 9
+ num_field: 39
+ fc_sizes: [400, 400, 400]
+ learning_rate: 0.0001
+ reg: 0.001
+ act: "relu"
+ optimizer: SGD
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/rank/deepfm/data/download_preprocess.py b/models/rank/deepfm/data/download_preprocess.py
new file mode 100755
index 000000000..e8c94cc64
--- /dev/null
+++ b/models/rank/deepfm/data/download_preprocess.py
@@ -0,0 +1,25 @@
+import os
+import shutil
+import sys
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from paddlerec.tools.tools import download_file_and_uncompress, download_file
+
+if __name__ == '__main__':
+ url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
+ url2 = "https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2"
+
+ print("download and extract starting...")
+ download_file_and_uncompress(url)
+ download_file(url2, "./aid_data/feat_dict_10.pkl2", True)
+ print("download and extract finished")
+
+ print("preprocessing...")
+ os.system("python preprocess.py")
+ print("preprocess done")
+
+ shutil.rmtree("raw_data")
+ print("done")
diff --git a/models/rank/deepfm/data/get_slot_data.py b/models/rank/deepfm/data/get_slot_data.py
new file mode 100755
index 000000000..59dc33b0d
--- /dev/null
+++ b/models/rank/deepfm/data/get_slot_data.py
@@ -0,0 +1,91 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import yaml
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+try:
+ import cPickle as pickle
+except ImportError:
+ import pickle
+class TrainReader(dg.MultiSlotDataGenerator):
+
+ def __init__(self, config):
+ dg.MultiSlotDataGenerator.__init__(self)
+
+ if os.path.isfile(config):
+ with open(config, 'r') as rb:
+ _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+ else:
+ raise ValueError("reader config only support yaml")
+
+ def init(self):
+ self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+ self.cont_max_ = [
+ 5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 46,
+ 231, 4008, 7393
+ ]
+ self.cont_diff_ = [
+ self.cont_max_[i] - self.cont_min_[i]
+ for i in range(len(self.cont_min_))
+ ]
+ self.continuous_range_ = range(1, 14)
+ self.categorical_range_ = range(14, 40)
+ # load preprocessed feature dict
+ self.feat_dict_name = "aid_data/feat_dict_10.pkl2"
+ self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
+
+ def _process_line(self, line):
+ features = line.rstrip('\n').split('\t')
+ feat_idx = []
+ feat_value = []
+ for idx in self.continuous_range_:
+ if features[idx] == '':
+ feat_idx.append(0)
+ feat_value.append(0.0)
+ else:
+ feat_idx.append(self.feat_dict_[idx])
+ feat_value.append(
+ (float(features[idx]) - self.cont_min_[idx - 1]) /
+ self.cont_diff_[idx - 1])
+ for idx in self.categorical_range_:
+ if features[idx] == '' or features[idx] not in self.feat_dict_:
+ feat_idx.append(0)
+ feat_value.append(0.0)
+ else:
+ feat_idx.append(self.feat_dict_[features[idx]])
+ feat_value.append(1.0)
+ label = [int(features[0])]
+ return feat_idx, feat_value, label
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def data_iter():
+ feat_idx, feat_value, label = self._process_line(line)
+ s = ""
+ for i in [('feat_idx', feat_idx), ('feat_value', feat_value), ('label', label)]:
+ k = i[0]
+ v = i[1]
+ for j in v:
+ s += " " + k + ":" + str(j)
+ print s.strip()
+ yield None
+ return data_iter
+
+reader = TrainReader("../config.yaml")
+reader.init()
+reader.run_from_stdin()
diff --git a/models/rank/deepfm/data/preprocess.py b/models/rank/deepfm/data/preprocess.py
new file mode 100755
index 000000000..1fa4a5fea
--- /dev/null
+++ b/models/rank/deepfm/data/preprocess.py
@@ -0,0 +1,101 @@
+import os
+import numpy
+from collections import Counter
+import shutil
+import pickle
+
+
+def get_raw_data():
+ if not os.path.isdir('raw_data'):
+ os.mkdir('raw_data')
+
+ fin = open('train.txt', 'r')
+ fout = open('raw_data/part-0', 'w')
+ for line_idx, line in enumerate(fin):
+ if line_idx % 200000 == 0 and line_idx != 0:
+ fout.close()
+ cur_part_idx = int(line_idx / 200000)
+ fout = open('raw_data/part-' + str(cur_part_idx), 'w')
+ fout.write(line)
+ fout.close()
+ fin.close()
+
+
+def split_data():
+ split_rate_ = 0.9
+ dir_train_file_idx_ = 'aid_data/train_file_idx.txt'
+ filelist_ = [
+ 'raw_data/part-%d' % x for x in range(len(os.listdir('raw_data')))
+ ]
+
+ if not os.path.exists(dir_train_file_idx_):
+ train_file_idx = list(
+ numpy.random.choice(
+ len(filelist_), int(len(filelist_) * split_rate_), False))
+ with open(dir_train_file_idx_, 'w') as fout:
+ fout.write(str(train_file_idx))
+ else:
+ with open(dir_train_file_idx_, 'r') as fin:
+ train_file_idx = eval(fin.read())
+
+ for idx in range(len(filelist_)):
+ if idx in train_file_idx:
+ shutil.move(filelist_[idx], 'train_data')
+ else:
+ shutil.move(filelist_[idx], 'test_data')
+
+
+def get_feat_dict():
+ freq_ = 10
+ dir_feat_dict_ = 'aid_data/feat_dict_' + str(freq_) + '.pkl2'
+ continuous_range_ = range(1, 14)
+ categorical_range_ = range(14, 40)
+
+ if not os.path.exists(dir_feat_dict_):
+ # print('generate a feature dict')
+ # Count the number of occurrences of discrete features
+ feat_cnt = Counter()
+ with open('train.txt', 'r') as fin:
+ for line_idx, line in enumerate(fin):
+ if line_idx % 100000 == 0:
+ print('generating feature dict', line_idx / 45000000)
+ features = line.rstrip('\n').split('\t')
+ for idx in categorical_range_:
+ if features[idx] == '': continue
+ feat_cnt.update([features[idx]])
+
+ # Only retain discrete features with high frequency
+ dis_feat_set = set()
+ for feat, ot in feat_cnt.items():
+ if ot >= freq_:
+ dis_feat_set.add(feat)
+
+ # Create a dictionary for continuous and discrete features
+ feat_dict = {}
+ tc = 1
+ # Continuous features
+ for idx in continuous_range_:
+ feat_dict[idx] = tc
+ tc += 1
+ for feat in dis_feat_set:
+ feat_dict[feat] = tc
+ tc += 1
+ # Save dictionary
+ with open(dir_feat_dict_, 'wb') as fout:
+ pickle.dump(feat_dict, fout, protocol=2)
+ print('args.num_feat ', len(feat_dict) + 1)
+
+
+if __name__ == '__main__':
+ if not os.path.isdir('train_data'):
+ os.mkdir('train_data')
+ if not os.path.isdir('test_data'):
+ os.mkdir('test_data')
+ if not os.path.isdir('aid_data'):
+ os.mkdir('aid_data')
+
+ get_raw_data()
+ split_data()
+ get_feat_dict()
+
+ print('Done!')
diff --git a/models/rank/deepfm/data/run.sh b/models/rank/deepfm/data/run.sh
new file mode 100644
index 000000000..c2bc4ae8c
--- /dev/null
+++ b/models/rank/deepfm/data/run.sh
@@ -0,0 +1,13 @@
+python download_preprocess.py
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+ cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+ cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done
diff --git a/models/rank/deepfm/model.py b/models/rank/deepfm/model.py
new file mode 100755
index 000000000..bfda02a21
--- /dev/null
+++ b/models/rank/deepfm/model.py
@@ -0,0 +1,154 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def deepfm_net(self):
+ init_value_ = 0.1
+ is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
+ sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
+ sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
+
+ # ------------------------- network input --------------------------
+
+ num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
+
+ raw_feat_idx = self._sparse_data_var[1]
+ raw_feat_value = self._dense_data_var[0]
+ self.label = self._sparse_data_var[0]
+
+ feat_idx = raw_feat_idx
+ feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
+
+ reg = envs.get_global_env("hyper_parameters.reg", 1e-4, self._namespace)
+ first_weights_re = fluid.embedding(
+ input=feat_idx,
+ is_sparse=True,
+ is_distributed=is_distributed,
+ dtype='float32',
+ size=[sparse_feature_number + 1, 1],
+ padding_idx=0,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_),
+ regularizer=fluid.regularizer.L1DecayRegularizer(reg)))
+ first_weights = fluid.layers.reshape(
+ first_weights_re, shape=[-1, num_field, 1]) # None * num_field * 1
+ y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1)
+
+ # ------------------------- second order term --------------------------
+
+ feat_embeddings_re = fluid.embedding(
+ input=feat_idx,
+ is_sparse=True,
+ is_distributed=is_distributed,
+ dtype='float32',
+ size=[sparse_feature_number + 1, sparse_feature_dim],
+ padding_idx=0,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_ / math.sqrt(float(sparse_feature_dim)))))
+ feat_embeddings = fluid.layers.reshape(
+ feat_embeddings_re,
+ shape=[-1, num_field,
+ sparse_feature_dim]) # None * num_field * embedding_size
+ feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
+
+ # sum_square part
+ summed_features_emb = fluid.layers.reduce_sum(feat_embeddings,
+ 1) # None * embedding_size
+ summed_features_emb_square = fluid.layers.square(
+ summed_features_emb) # None * embedding_size
+
+ # square_sum part
+ squared_features_emb = fluid.layers.square(
+ feat_embeddings) # None * num_field * embedding_size
+ squared_sum_features_emb = fluid.layers.reduce_sum(
+ squared_features_emb, 1) # None * embedding_size
+
+ y_second_order = 0.5 * fluid.layers.reduce_sum(
+ summed_features_emb_square - squared_sum_features_emb, 1,
+ keep_dim=True) # None * 1
+
+ # ------------------------- DNN --------------------------
+
+ layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
+ act = envs.get_global_env("hyper_parameters.act", None, self._namespace)
+ y_dnn = fluid.layers.reshape(feat_embeddings,
+ [-1, num_field * sparse_feature_dim])
+ for s in layer_sizes:
+ y_dnn = fluid.layers.fc(
+ input=y_dnn,
+ size=s,
+ act=act,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_ / math.sqrt(float(10)))),
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_)))
+ y_dnn = fluid.layers.fc(
+ input=y_dnn,
+ size=1,
+ act=None,
+ param_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_)),
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.TruncatedNormalInitializer(
+ loc=0.0, scale=init_value_)))
+
+ # ------------------------- DeepFM --------------------------
+
+ self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + y_dnn)
+
+ def train_net(self):
+ self.model._init_slots()
+ self.deepfm_net()
+
+ # ------------------------- Cost(logloss) --------------------------
+
+ cost = fluid.layers.log_loss(input=self.predict, label=fluid.layers.cast(self.label, "float32"))
+ avg_cost = fluid.layers.reduce_sum(cost)
+
+ self._cost = avg_cost
+
+ # ------------------------- Metric(Auc) --------------------------
+
+ predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
+ label_int = fluid.layers.cast(self.label, 'int64')
+ auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
+ label=label_int,
+ slide_steps=0)
+ self._metrics["AUC"] = auc_var
+ self._metrics["BATCH_AUC"] = batch_auc_var
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
+ return optimizer
+
+ def infer_net(self, parameter_list):
+ self.model._init_slots()
+ self.deepfm_net()
diff --git a/models/rank/din/__init__.py b/models/rank/din/__init__.py
new file mode 100755
index 000000000..abf198b97
--- /dev/null
+++ b/models/rank/din/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/models/rank/din/config.yaml b/models/rank/din/config.yaml
new file mode 100755
index 000000000..bdf56b825
--- /dev/null
+++ b/models/rank/din/config.yaml
@@ -0,0 +1,51 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+train:
+ trainer:
+ # for cluster training
+ strategy: "async"
+
+ epochs: 10
+ workspace: "paddlerec.models.rank.din"
+
+ reader:
+ batch_size: 2
+ class: "{workspace}/reader.py"
+ train_data_path: "{workspace}/data/train_data"
+ dataset_class: "DataLoader"
+
+ model:
+ models: "{workspace}/model.py"
+ hyper_parameters:
+ use_DataLoader: True
+ item_emb_size: 64
+ cat_emb_size: 64
+ is_sparse: False
+ config_path: "data/config.txt"
+ fc_sizes: [400, 400, 400]
+ learning_rate: 0.0001
+ reg: 0.001
+ act: "sigmoid"
+ optimizer: SGD
+
+ save:
+ increment:
+ dirname: "increment"
+ epoch_interval: 2
+ save_last: True
+ inference:
+ dirname: "inference"
+ epoch_interval: 4
+ save_last: True
diff --git a/models/rank/din/data/build_dataset.py b/models/rank/din/data/build_dataset.py
new file mode 100755
index 000000000..34c053ccd
--- /dev/null
+++ b/models/rank/din/data/build_dataset.py
@@ -0,0 +1,87 @@
+from __future__ import print_function
+import random
+import pickle
+
+random.seed(1234)
+
+print("read and process data")
+
+with open('./raw_data/remap.pkl', 'rb') as f:
+ reviews_df = pickle.load(f)
+ cate_list = pickle.load(f)
+ user_count, item_count, cate_count, example_count = pickle.load(f)
+
+train_set = []
+test_set = []
+for reviewerID, hist in reviews_df.groupby('reviewerID'):
+ pos_list = hist['asin'].tolist()
+
+ def gen_neg():
+ neg = pos_list[0]
+ while neg in pos_list:
+ neg = random.randint(0, item_count - 1)
+ return neg
+
+ neg_list = [gen_neg() for i in range(len(pos_list))]
+
+ for i in range(1, len(pos_list)):
+ hist = pos_list[:i]
+ if i != len(pos_list) - 1:
+ train_set.append((reviewerID, hist, pos_list[i], 1))
+ train_set.append((reviewerID, hist, neg_list[i], 0))
+ else:
+ label = (pos_list[i], neg_list[i])
+ test_set.append((reviewerID, hist, label))
+
+random.shuffle(train_set)
+random.shuffle(test_set)
+
+assert len(test_set) == user_count
+
+
+def print_to_file(data, fout):
+ for i in range(len(data)):
+ fout.write(str(data[i]))
+ if i != len(data) - 1:
+ fout.write(' ')
+ else:
+ fout.write(';')
+
+
+print("make train data")
+with open("paddle_train.txt", "w") as fout:
+ for line in train_set:
+ history = line[1]
+ target = line[2]
+ label = line[3]
+ cate = [cate_list[x] for x in history]
+ print_to_file(history, fout)
+ print_to_file(cate, fout)
+ fout.write(str(target) + ";")
+ fout.write(str(cate_list[target]) + ";")
+ fout.write(str(label) + "\n")
+
+print("make test data")
+with open("paddle_test.txt", "w") as fout:
+ for line in test_set:
+ history = line[1]
+ target = line[2]
+ cate = [cate_list[x] for x in history]
+
+ print_to_file(history, fout)
+ print_to_file(cate, fout)
+ fout.write(str(target[0]) + ";")
+ fout.write(str(cate_list[target[0]]) + ";")
+ fout.write("1\n")
+
+ print_to_file(history, fout)
+ print_to_file(cate, fout)
+ fout.write(str(target[1]) + ";")
+ fout.write(str(cate_list[target[1]]) + ";")
+ fout.write("0\n")
+
+print("make config data")
+with open('config.txt', 'w') as f:
+ f.write(str(user_count) + "\n")
+ f.write(str(item_count) + "\n")
+ f.write(str(cate_count) + "\n")
diff --git a/models/rank/din/data/convert_pd.py b/models/rank/din/data/convert_pd.py
new file mode 100755
index 000000000..d7927c7ef
--- /dev/null
+++ b/models/rank/din/data/convert_pd.py
@@ -0,0 +1,27 @@
+from __future__ import print_function
+import pickle
+import pandas as pd
+
+
+def to_df(file_path):
+ with open(file_path, 'r') as fin:
+ df = {}
+ i = 0
+ for line in fin:
+ df[i] = eval(line)
+ i += 1
+ df = pd.DataFrame.from_dict(df, orient='index')
+ return df
+
+
+print("start to analyse reviews_Electronics_5.json")
+reviews_df = to_df('./raw_data/reviews_Electronics_5.json')
+with open('./raw_data/reviews.pkl', 'wb') as f:
+ pickle.dump(reviews_df, f, pickle.HIGHEST_PROTOCOL)
+
+print("start to analyse meta_Electronics.json")
+meta_df = to_df('./raw_data/meta_Electronics.json')
+meta_df = meta_df[meta_df['asin'].isin(reviews_df['asin'].unique())]
+meta_df = meta_df.reset_index(drop=True)
+with open('./raw_data/meta.pkl', 'wb') as f:
+ pickle.dump(meta_df, f, pickle.HIGHEST_PROTOCOL)
diff --git a/models/rank/din/data/data_process.sh b/models/rank/din/data/data_process.sh
new file mode 100755
index 000000000..7bcfc55f4
--- /dev/null
+++ b/models/rank/din/data/data_process.sh
@@ -0,0 +1,15 @@
+#! /bin/bash
+
+set -e
+echo "begin download data"
+mkdir raw_data
+cd raw_data
+wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+gzip -d reviews_Electronics_5.json.gz
+wget -c http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Electronics.json.gz
+gzip -d meta_Electronics.json.gz
+echo "download data successfully"
+
+cd ..
+python convert_pd.py
+python remap_id.py
diff --git a/models/rank/din/data/remap_id.py b/models/rank/din/data/remap_id.py
new file mode 100755
index 000000000..b110dac54
--- /dev/null
+++ b/models/rank/din/data/remap_id.py
@@ -0,0 +1,48 @@
+from __future__ import print_function
+import random
+import pickle
+import numpy as np
+
+random.seed(1234)
+
+with open('./raw_data/reviews.pkl', 'rb') as f:
+ reviews_df = pickle.load(f)
+ reviews_df = reviews_df[['reviewerID', 'asin', 'unixReviewTime']]
+with open('./raw_data/meta.pkl', 'rb') as f:
+ meta_df = pickle.load(f)
+ meta_df = meta_df[['asin', 'categories']]
+ meta_df['categories'] = meta_df['categories'].map(lambda x: x[-1][-1])
+
+
+def build_map(df, col_name):
+ key = sorted(df[col_name].unique().tolist())
+ m = dict(zip(key, range(len(key))))
+ df[col_name] = df[col_name].map(lambda x: m[x])
+ return m, key
+
+
+asin_map, asin_key = build_map(meta_df, 'asin')
+cate_map, cate_key = build_map(meta_df, 'categories')
+revi_map, revi_key = build_map(reviews_df, 'reviewerID')
+
+user_count, item_count, cate_count, example_count =\
+ len(revi_map), len(asin_map), len(cate_map), reviews_df.shape[0]
+print('user_count: %d\titem_count: %d\tcate_count: %d\texample_count: %d' %
+ (user_count, item_count, cate_count, example_count))
+
+meta_df = meta_df.sort_values('asin')
+meta_df = meta_df.reset_index(drop=True)
+reviews_df['asin'] = reviews_df['asin'].map(lambda x: asin_map[x])
+reviews_df = reviews_df.sort_values(['reviewerID', 'unixReviewTime'])
+reviews_df = reviews_df.reset_index(drop=True)
+reviews_df = reviews_df[['reviewerID', 'asin', 'unixReviewTime']]
+
+cate_list = [meta_df['categories'][i] for i in range(len(asin_map))]
+cate_list = np.array(cate_list, dtype=np.int32)
+
+with open('./raw_data/remap.pkl', 'wb') as f:
+ pickle.dump(reviews_df, f, pickle.HIGHEST_PROTOCOL) # uid, iid
+ pickle.dump(cate_list, f, pickle.HIGHEST_PROTOCOL) # cid of iid line
+ pickle.dump((user_count, item_count, cate_count, example_count), f,
+ pickle.HIGHEST_PROTOCOL)
+ pickle.dump((asin_key, cate_key, revi_key), f, pickle.HIGHEST_PROTOCOL)
diff --git a/models/rank/din/data/train_data/paddle_train.100.txt b/models/rank/din/data/train_data/paddle_train.100.txt
new file mode 100755
index 000000000..2696a2917
--- /dev/null
+++ b/models/rank/din/data/train_data/paddle_train.100.txt
@@ -0,0 +1,100 @@
+3737 19450;288 196;18486;674;1
+3647 4342 6855 3805;281 463 558 674;4206;463;1
+1805 4309;87 87;21354;556;1
+18209 20753;649 241;51924;610;0
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diff --git a/models/rank/din/model.py b/models/rank/din/model.py
new file mode 100755
index 000000000..2abc658b6
--- /dev/null
+++ b/models/rank/din/model.py
@@ -0,0 +1,194 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+ def __init__(self, config):
+ ModelBase.__init__(self, config)
+
+ def config_read(self, config_path):
+ with open(config_path, "r") as fin:
+ user_count = int(fin.readline().strip())
+ item_count = int(fin.readline().strip())
+ cat_count = int(fin.readline().strip())
+ return user_count, item_count, cat_count
+
+ def din_attention(self, hist, target_expand, mask):
+ """activation weight"""
+
+ hidden_size = hist.shape[-1]
+
+ concat = fluid.layers.concat(
+ [hist, target_expand, hist - target_expand, hist * target_expand],
+ axis=2)
+ atten_fc1 = fluid.layers.fc(name="atten_fc1",
+ input=concat,
+ size=80,
+ act=self.act,
+ num_flatten_dims=2)
+ atten_fc2 = fluid.layers.fc(name="atten_fc2",
+ input=atten_fc1,
+ size=40,
+ act=self.act,
+ num_flatten_dims=2)
+ atten_fc3 = fluid.layers.fc(name="atten_fc3",
+ input=atten_fc2,
+ size=1,
+ num_flatten_dims=2)
+ atten_fc3 += mask
+ atten_fc3 = fluid.layers.transpose(x=atten_fc3, perm=[0, 2, 1])
+ atten_fc3 = fluid.layers.scale(x=atten_fc3, scale=hidden_size**-0.5)
+ weight = fluid.layers.softmax(atten_fc3)
+ out = fluid.layers.matmul(weight, hist)
+ out = fluid.layers.reshape(x=out, shape=[0, hidden_size])
+ return out
+
+ def train_net(self):
+ seq_len = -1
+ self.item_emb_size = envs.get_global_env("hyper_parameters.item_emb_size", 64, self._namespace)
+ self.cat_emb_size = envs.get_global_env("hyper_parameters.cat_emb_size", 64, self._namespace)
+ self.act = envs.get_global_env("hyper_parameters.act", "sigmoid", self._namespace)
+ #item_emb_size = 64
+ #cat_emb_size = 64
+ self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", False, self._namespace)
+ #significant for speeding up the training process
+ self.config_path = envs.get_global_env("hyper_parameters.config_path", "data/config.txt", self._namespace)
+ self.use_DataLoader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
+ user_count, item_count, cat_count = self.config_read(self.config_path)
+
+
+ item_emb_attr = fluid.ParamAttr(name="item_emb")
+ cat_emb_attr = fluid.ParamAttr(name="cat_emb")
+
+ hist_item_seq = fluid.data(
+ name="hist_item_seq", shape=[None, seq_len], dtype="int64")
+ self._data_var.append(hist_item_seq)
+
+ hist_cat_seq = fluid.data(
+ name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
+ self._data_var.append(hist_cat_seq)
+
+ target_item = fluid.data(name="target_item", shape=[None], dtype="int64")
+ self._data_var.append(target_item)
+
+ target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
+ self._data_var.append(target_cat)
+
+ label = fluid.data(name="label", shape=[None, 1], dtype="float32")
+ self._data_var.append(label)
+
+ mask = fluid.data(name="mask", shape=[None, seq_len, 1], dtype="float32")
+ self._data_var.append(mask)
+
+ target_item_seq = fluid.data(
+ name="target_item_seq", shape=[None, seq_len], dtype="int64")
+ self._data_var.append(target_item_seq)
+
+ target_cat_seq = fluid.data(
+ name="target_cat_seq", shape=[None, seq_len], dtype="int64")
+ self._data_var.append(target_cat_seq)
+
+ if self.use_DataLoader:
+ self._data_loader = fluid.io.DataLoader.from_generator(
+ feed_list=self._data_var, capacity=10000, use_double_buffer=False, iterable=False)
+
+ hist_item_emb = fluid.embedding(
+ input=hist_item_seq,
+ size=[item_count, self.item_emb_size],
+ param_attr=item_emb_attr,
+ is_sparse=self.is_sparse)
+
+ hist_cat_emb = fluid.embedding(
+ input=hist_cat_seq,
+ size=[cat_count, self.cat_emb_size],
+ param_attr=cat_emb_attr,
+ is_sparse=self.is_sparse)
+
+ target_item_emb = fluid.embedding(
+ input=target_item,
+ size=[item_count, self.item_emb_size],
+ param_attr=item_emb_attr,
+ is_sparse=self.is_sparse)
+
+ target_cat_emb = fluid.embedding(
+ input=target_cat,
+ size=[cat_count, self.cat_emb_size],
+ param_attr=cat_emb_attr,
+ is_sparse=self.is_sparse)
+
+ target_item_seq_emb = fluid.embedding(
+ input=target_item_seq,
+ size=[item_count, self.item_emb_size],
+ param_attr=item_emb_attr,
+ is_sparse=self.is_sparse)
+
+ target_cat_seq_emb = fluid.embedding(
+ input=target_cat_seq,
+ size=[cat_count, self.cat_emb_size],
+ param_attr=cat_emb_attr,
+ is_sparse=self.is_sparse)
+
+ item_b = fluid.embedding(
+ input=target_item,
+ size=[item_count, 1],
+ param_attr=fluid.initializer.Constant(value=0.0))
+
+ hist_seq_concat = fluid.layers.concat([hist_item_emb, hist_cat_emb], axis=2)
+ target_seq_concat = fluid.layers.concat(
+ [target_item_seq_emb, target_cat_seq_emb], axis=2)
+ target_concat = fluid.layers.concat(
+ [target_item_emb, target_cat_emb], axis=1)
+
+ out = self.din_attention(hist_seq_concat, target_seq_concat, mask)
+ out_fc = fluid.layers.fc(name="out_fc",
+ input=out,
+ size=self.item_emb_size + self.cat_emb_size,
+ num_flatten_dims=1)
+ embedding_concat = fluid.layers.concat([out_fc, target_concat], axis=1)
+
+ fc1 = fluid.layers.fc(name="fc1",
+ input=embedding_concat,
+ size=80,
+ act=self.act)
+ fc2 = fluid.layers.fc(name="fc2", input=fc1, size=40, act=self.act)
+ fc3 = fluid.layers.fc(name="fc3", input=fc2, size=1)
+ logit = fc3 + item_b
+
+ loss = fluid.layers.sigmoid_cross_entropy_with_logits(x=logit, label=label)
+
+ avg_loss = fluid.layers.mean(loss)
+ self._cost = avg_loss
+
+ self.predict = fluid.layers.sigmoid(logit)
+ predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
+ label_int = fluid.layers.cast(label, 'int64')
+ auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
+ label=label_int,
+ slide_steps=0)
+ self._metrics["AUC"] = auc_var
+ self._metrics["BATCH_AUC"] = batch_auc_var
+
+
+ def optimizer(self):
+ learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+ optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
+ return optimizer
+
+ def infer_net(self, parameter_list):
+ self.deepfm_net()
diff --git a/models/rank/din/reader.py b/models/rank/din/reader.py
new file mode 100755
index 000000000..39ed690fd
--- /dev/null
+++ b/models/rank/din/reader.py
@@ -0,0 +1,130 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import os
+import random
+
+try:
+ import cPickle as pickle
+except ImportError:
+ import pickle
+
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
+class TrainReader(Reader):
+ def init(self):
+ self.train_data_path = envs.get_global_env("train_data_path", None, "train.reader")
+ self.res = []
+ self.max_len = 0
+
+ data_file_list = os.listdir(self.train_data_path)
+ for i in range(0, len(data_file_list)):
+ train_data_file = os.path.join(self.train_data_path, data_file_list[i])
+ with open(train_data_file, "r") as fin:
+ for line in fin:
+ line = line.strip().split(';')
+ hist = line[0].split()
+ self.max_len = max(self.max_len, len(hist))
+ fo = open("tmp.txt", "w")
+ fo.write(str(self.max_len))
+ fo.close()
+ self.batch_size = envs.get_global_env("batch_size", 32, "train.reader")
+ self.group_size = self.batch_size * 20
+
+ def _process_line(self, line):
+ line = line.strip().split(';')
+ hist = line[0].split()
+ hist = [int(i) for i in hist]
+ cate = line[1].split()
+ cate = [int(i) for i in cate]
+ return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
+
+ def generate_sample(self, line):
+ """
+ Read the data line by line and process it as a dictionary
+ """
+
+ def data_iter():
+ # feat_idx, feat_value, label = self._process_line(line)
+ yield self._process_line(line)
+
+ return data_iter
+
+ def pad_batch_data(self, input, max_len):
+ res = np.array([x + [0] * (max_len - len(x)) for x in input])
+ res = res.astype("int64").reshape([-1, max_len])
+ return res
+
+ def make_data(self, b):
+ max_len = max(len(x[0]) for x in b)
+ item = self.pad_batch_data([x[0] for x in b], max_len)
+ cat = self.pad_batch_data([x[1] for x in b], max_len)
+ len_array = [len(x[0]) for x in b]
+ mask = np.array(
+ [[0] * x + [-1e9] * (max_len - x) for x in len_array]).reshape(
+ [-1, max_len, 1])
+ target_item_seq = np.array(
+ [[x[2]] * max_len for x in b]).astype("int64").reshape([-1, max_len])
+ target_cat_seq = np.array(
+ [[x[3]] * max_len for x in b]).astype("int64").reshape([-1, max_len])
+ res = []
+ for i in range(len(b)):
+ res.append([
+ item[i], cat[i], b[i][2], b[i][3], b[i][4], mask[i],
+ target_item_seq[i], target_cat_seq[i]
+ ])
+ return res
+
+ def batch_reader(self, reader, batch_size, group_size):
+ def batch_reader():
+ bg = []
+ for line in reader:
+ bg.append(line)
+ if len(bg) == group_size:
+ sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+ bg = []
+ for i in range(0, group_size, batch_size):
+ b = sortb[i:i + batch_size]
+ yield self.make_data(b)
+ len_bg = len(bg)
+ if len_bg != 0:
+ sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+ bg = []
+ remain = len_bg % batch_size
+ for i in range(0, len_bg - remain, batch_size):
+ b = sortb[i:i + batch_size]
+ yield self.make_data(b)
+
+ return batch_reader
+
+ def base_read(self, file_dir):
+ res = []
+ for train_file in file_dir:
+ with open(train_file, "r") as fin:
+ for line in fin:
+ line = line.strip().split(';')
+ hist = line[0].split()
+ cate = line[1].split()
+ res.append([hist, cate, line[2], line[3], float(line[4])])
+ return res
+
+ def generate_batch_from_trainfiles(self, files):
+ data_set = self.base_read(files)
+ random.shuffle(data_set)
+ return self.batch_reader(data_set, self.batch_size, self.batch_size * 20)
diff --git a/models/rank/dnn/README.md b/models/rank/dnn/README.md
new file mode 100644
index 000000000..c307efcc2
--- /dev/null
+++ b/models/rank/dnn/README.md
@@ -0,0 +1,261 @@
+# 基于DNN模型的点击率预估模型
+
+## 介绍
+`CTR(Click Through Rate)`，即点击率，是“推荐系统/计算广告”等领域的重要指标，对其进行预估是商品推送/广告投放等决策的基础。简单来说，CTR预估对每次广告的点击情况做出预测，预测用户是点击还是不点击。CTR预估模型综合考虑各种因素、特征，在大量历史数据上训练，最终对商业决策提供帮助。本模型实现了下述论文中提出的DNN模型：
+
+```text
+@inproceedings{guo2017deepfm,
+ title={DeepFM: A Factorization-Machine based Neural Network for CTR Prediction},
+ author={Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li and Xiuqiang He},
+ booktitle={the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI)},
+ pages={1725--1731},
+ year={2017}
+}
+```
+
+#
+## 数据准备
+### 数据来源
+训练及测试数据集选用[Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge/)所用的Criteo数据集。该数据集包括两部分：训练集和测试集。训练集包含一段时间内Criteo的部分流量，测试集则对应训练数据后一天的广告点击流量。
+每一行数据格式如下所示：
+```bash
+
+
+
+
+